绿色金融 投资组合

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This research paper responds to the growing global demand for environmentally and socially responsible financial practices by outlining a strong framework for incorporating sustainable and green finance into effective trading portfolio management. The study acknowledges the current difficulties of reconciling financial goals with sustainability criteria and uses a methodological approach that includes risk-sensitive asset allocation, mean-variance optimization, and strategic maximization of the Sharpe ratio. By carefully examining and analyzing this research, it explores the complex dynamics of sustainable finance, thus providing a holistic understanding of how financial success is related to environmental and social responsibility. The findings of this study provide important insights into ongoing discussions on responsible investment strategies, thereby giving investors and policymakers a guide on how to align their financial objectives with sustainable development imperatives.

Abstract This paper introduces DivFolio, a multiperiod portfolio selection and analytic software application that incorporates automated and user-determined divestment practices accommodating Environmental Social Governance (ESG) and portfolio carbon footprint considerations. This freely available portfolio analytics software tool is written in R with a GUI interface developed as an R Shiny application for ease of user experience. Users can utilize this software to dynamically assess the performance of asset selections from global equity, exchange-traded funds, exchange-traded notes, and depositary receipts markets over multiple time periods. This assessment is based on the impact of ESG investment and fossil-fuel divestment practices on portfolio behavior in terms of risk, return, stability, diversification, and climate mitigation credentials of associated investment decisions. We highlight two applications of DivFolio. The first revolves around using sector scanning to divest from a specialized portfolio featuring constituents of the FTSE 100. The second, rooted in actuarial considerations, focuses on divestment strategies informed by environmental risk assessments for mixed pension portfolios in the US and UK.

In this article, we attempt to re-examine and assess the hedging effectiveness of Gold and Bitcoin against Blue Economy and Green Finance Exchange-Traded Funds (ETFs). To that end, we investigate the dynamic pairwise interaction among different asset classes and then compute different optimal hedge ratios. We use the Copula approach to produce the optimal hedge ratios for all pairwise combinations and the hedging effectiveness indexes. The time-varying correlations among Gold/Bitcoin and Blue/Green assets indicate dynamic features of the relationship among such asset classes. Gold offers a better hedge than Bitcoin in terms of optimal hedge ratio and hedging effectiveness. Time-varying hedging ratios and effectiveness are well-documented, showing dynamic cross-hedging Bitcoin/Gold-Blue/green ETFs. Such results are insightful for investors and portfolio managers, pointing to an ongoing regular demand to rebalance their portfolios.

PurposeWe examined the dynamic volatility connectedness and diversification strategies among US real estate investment trusts (REITs) and green finance indices.Design/methodology/approachThe DCC-GARCH dynamic connectedness framework and he DCC-GARCH t-copula model were employed in this study.FindingsUsing daily data from 2,206 observations spanning from 2 January 2015 to 31 January 2023 this paper presents the following findings: (1) cross-market spillovers exhibited a high correlation and significant fluctuations, particularly during extreme events; (2) our analysis confirmed that REIT acted as net receivers from other green indices, with the S&P North America Large-MidCap Carbon Efficient Index dominating the in-network volatility spillover; (3) this observation suggests asymmetric spillovers between the two markets and (4) a portfolio analysis was conducted using the DCC-GARCH t-copula framework to estimate hedging ratios and portfolio weights for these indices. When REIT and the Dow Jones US Select ESG REIT Index were simultaneously added to a risk-hedged portfolio, our findings indicated that no risk-hedging effect could be achieved. Moreover, the cost and performance of hedging green assets using REIT were found to be comparable.Originality/valueWe first examined the dynamic volatility connectedness and diversification strategies among US REITs and green finance indices. The outcomes of this study carry practical implications for market participants.

In this article, we study the relevance of green finance from a portfolio and a network perspective. The estimates are derived from a regularized graphical model, which allows us to deal with two important issues. First, we refer to the curse of dimensionality, as we focus on a relatively large set of companies. Second, we explicitly take into account the heavy‐tailed distributions of financial time series, which reflect the impact of crises and recessions. Focusing on a time interval spanning across well‐known tail events, from the US subprime crisis to the recent outbreak of the COVID‐19 pandemic, we show that the selected green stocks offer a relevant contribution to the minimization of the overall portfolio risk. Moreover, they outperform the gray assets in terms of risk, profitability, and risk‐adjusted return in a statistically significant way. These findings are consistent with the estimates obtained from the network analysis. Indeed, the gray stocks exhibit a greater connection within the dynamic networks and, then, are more exposed to the risk of a greater propagation of negative spillover effects during stressed periods. Interestingly, the relevance of the green stocks increases when moving from the standard Gaussian to the leptokurtic setting. The policy implications suggested by these results induce policymakers to undertake synergistic interventions with private finance aimed at supporting a green economy and environmentally responsible companies.

: Green investments are currently gaining investor attention. The question investors are addressing is to what extent investing in environmental companies or funds increases the risk/reward ratio. The novelty of the theoretical approach lies in the construction of a newly developed portfolio selection model with embedded environmental, social and governance (ESG) indicators that also represent the values of the environmental criteria. This innovative model, based on a portfolio selection model using conditional value at risk (CVaR) measures and analyzed data for the Standard and Poor’s 500 (S&P 500) stock index, offers a promising potential for investors. The paper’s core is the section where the authors define possible approaches to solve the portfolio selection problem based on the selected environmental criterion E. The analysis itself is contained in section three, where the three variants—the absence of environmental concerns, the maximum preference for environmental requirements, and the combined approach of constructing a portfolio on a defined set of stocks—are analyzed, which is the result of the authors’ specific approach. Through a comprehensive analysis, this paper focuses on the aspect of determining the amount of risk difference in the approach of selecting assets with the required value of the relevant indicator. This thorough analysis ensures a narrowing of the set of potential assets in the portfolio, regardless of the amount of their return, and assets for which the group condition on the value of the relevant indicator can be applied, which based on the group condition, allows one to include assets with a higher return in the portfolio. The research results confirm that it is preferable for an investor to consider the environmental criteria on the portfolio as a whole rather than on individual assets

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This study examines the aftermarket performance of high-green and low-green IPO and how green IPOs can optimize portfolio allocation. We assume the higher level of greenness increases investors' participation in IPOs. To this end, we develop the utility function and determine that investors prefer to participate in new issues when firms account for greenness measures. This study proposes the global aspects of green measure: the desired level of greenness a firm maintains. We find that IPOs in our sample are far below the global standards of greenness. This evidence suggests they must adopt the necessary actions to make the environment green. Another significant contribution of this study is to measure the performance of high and low-green IPOs in short- and long-run horizons. This study reveals that high-green IPOs are less underpriced. This study estimates the effect of greenness on initial returns and finds an inverse relationship suggesting that high-green IPOs are less underpriced due to lower risk associated with new issues. In terms of measuring longer-term performance, this study determines that high-green IPOs underperform less than low-green IPOs.

The use of artificial intelligence (AI) in green finance has become essential considering the growing environmental concerns and the need to slow down climate change. This abstract outline the essential role that artificial intelligence (AI) plays in propelling green investment, explaining its diverse functions and transformational potential. Primarily, artificial intelligence enhances the effectiveness of green finance by enabling data-driven decision making procedures.AI helps investors evaluate environmental risks, find sustainable investment opportunities, and allocate their portfolio optimally to green assets by using sophisticated algorithms and predictive analytics. Additionally, AI-powered platforms improve accountability and transparency, which strengthens investor confidence in green financial instruments. Second, AI enables regulatory agencies and financial institutions to reduce the risks related to environmental degradation and climate change. Stakeholders can protect financial stability and reduce systemic risks by proactively assessing an investment's resistance to climate related shocks by utilising AI driven risk assessment tools. Finally, AI encourages cooperation and knowledge exchange within the ecosystem of green finance. AI-driven platforms help disseminate knowledge by analysing large datasets and finding patterns. This allows researchers, policymakers, and investors to learn about new trends, best practices, and investment opportunities in sustainable finance.

This study examined the role of artificial intelligence (AI)-driven Environmental, Social and Governance (ESG) analytics in enhancing sustainable investment performance. While traditional ESG ratings had been widely used in responsible investment strategies, they often suffered from data inconsistency, subjectivity and limited coverage of unstructured sustainability information. AI-based ESG systems were increasingly applied to extract deeper sustainability signals from corporate disclosures, reports and external data sources. Using portfolio-level analysis, this study compared the financial outcomes of portfolios constructed using AI-driven ESG indicators with those based on conventional ESG ratings. The results showed that AI-enhanced high-ESG portfolios achieved higher mean returns and superior Sharpe ratios than both AI-based low-ESG portfolios and traditionally rated ESG portfolios. In addition, AI-driven high-ESG portfolios demonstrated lower downside-risk exposure and smaller maximum drawdowns during market stress, indicating stronger resilience. Regression analysis further revealed that AI-derived ESG scores were more strongly associated with excess returns than traditional ESG metrics. These findings suggested that AI improved the informational efficiency of ESG assessment by capturing more accurate, forward-looking sustainability risks and opportunities. The study concluded that AI-driven ESG analytics strengthened the financial relevance of sustainability integration and supported better-informed investment decision-making. The results carried important implications for investors, regulators and corporations seeking to align AI deployment with high-integrity sustainable finance practices, while also highlighting the need for ethical and transparent AI governance in financial markets. References Albuquerque, R., Koskinen, Y., Yang, S., & Zhang, C. (2020). 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Amidst the global push for decarbonization, green hydrogen has gained recognition as a versatile and clean energy carrier, prompting the financial sector to introduce specialized investment instruments like Green Hydrogen Exchange-Traded Funds (ETFs). Despite the nascent nature of research on green hydrogen portfolio performance, this study examines two key green hydrogen ETFs (i.e., HJEN and HDRO) from April 2021–May 2023, aiming at conducting a multifaceted exploration of their performance, isolating and measuring their sensitivity to the primary market factor, and assessing the capabilities of systematic trading strategies to preserve capital and minimize losses during market downturns. The results spotlight lower returns and higher risks in green hydrogen investments compared to conventional equity (proxied by ETFs offering exposure to developed markets—EFA and emerging markets—EEM) and green energy portfolios (proxied by the ETF ICLN). To comprehensively evaluate performance, an array of risk-adjusted metrics, including Std Sharpe, ES Sharpe, VaR Sharpe, Information ratio, Sortino ratio, Treynor ratio, and various downside risk metrics (historical VaR, modified VaR, Expected Shortfall, loss deviation, downside deviation, and maximum drawdown) are employed, offering a nuanced understanding of the investment landscape. Moreover, single-factor models highlight significant systematic market risk, reflected in notably high beta coefficients, negative alphas, and active premia, underscoring the sensitivity of green hydrogen investments to market fluctuations. Despite these challenges, a silver lining emerges as the study demonstrates the efficacy of implementing straightforward Dual Moving Average Crossover (DMAC) trading strategies. These strategies significantly enhance the risk-return profile of green hydrogen portfolios, offering investors a pathway to align financial and social objectives within their equity portfolios. This research is motivated by the need to provide market players, policymakers, and stakeholders with valuable insights into the benefits and risks associated with green hydrogen investment, considering its potential to reshape the global energy landscape.

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This research examines the influence of environmental risk management (ERM) on the credit portfolio stability of commercial banks in Uzbekistan, utilising secondary data from sustainability reports, regulatory publications, and international financial institutions. The results show that banks with more advanced ERM frameworks, which include environmental screening, green lending, and sustainability governance, have lower non-performing loan (NPL) ratios and better asset quality. On the other hand, banks that don’t use ERM as much are still more vulnerable to environmental and credit risks. The study finds that integrating environmental risks into the banking system in Uzbekistan is necessary to make it more financially stable and in line with global standards for sustainable finance

The global shift toward a low-carbon economy has intensified regulatory, market, investor, and societal pressures on banks to align lending portfolios with sustainable finance principles. In Indonesia, where coal remains central to the energy mix, banks face a strategic trade-off between sustaining profitability from coal financing and complying with green taxonomy requirements. This study evaluates the profitability implications of the coal sector phase-out for Bank Permata, a leading Indonesian commercial bank, and examines mitigation strategies. The analysis integrates Signaling Theory, Resource Dependence Theory, Stakeholder Theory, and Sustainable Finance to frame the strategic, risk, and stakeholder considerations in portfolio reallocation. A quantitative scenario analysis was applied to four publicly listed coal companies with existing credit facilities at Bank Permata, which were selected as a sample. Using a profit planning approach, financial projections were developed for income statements, balance sheets, and cash flows based on public disclosures and validated assumptions. Results were compared across a baseline (no phase out) and three phase out scenarios, with the most stringent targeting zero exposure by 2030. Findings indicate that phasing out in the short term will affect declines in interest income, driven by reduced loan balances and yields, but highlight long-term benefits through reduced regulatory non-compliance risk and lower reputational exposure to transition risks. Potential losses can also be mitigated by reallocating to green taxonomy-aligned sectors with competitive yields. The research offers a replicable scenario-based modeling framework for quantifying the financial effects of coal phase-out strategies in emerging market banking. It underscores the importance of strategic portfolio realignment, diversification into sustainable sectors, and strengthened ESG risk assessments to maintain profitability while supporting national and global sustainability goals.

Clean energy, with its focus on environmental sustainability and efficiency, has gained significance as concerns over the impact of traditional energy growth. However, there is limited evidence on the value of clean energy investments. This paper explores the role of clean energy in a balanced investment portfolio by examining two traditional energy assets (crude oil and natural gas) and two clean energy assets (SPDR S&P Kensho Clean Power ETF and iShares Global Clean Energy ETF). Using a time-varying parameter vector autoregression (TVP-VAR) model on daily data from October 2021 to January 2024, we analyze the evolving connectedness between these assets. Our results highlight dynamic interactions, with green finance indices like CNRG acting as net shock transmitters, while traditional energy indices, such as WTI and gas, primarily receive shocks. The analysis suggests that green assets, particularly ICLN, enhance portfolio stability and hedging efficiency, especially in minimum correlation and risk parity portfolios. Fossil fuels, especially gas, exhibit higher volatility, requiring careful portfolio management. Ultimately, integrating ESG criteria and adapting investment strategies to market conditions may enhance responsible investing and long-term value creation.

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This paper examines the interconnection and wavelet coherence between the green cryptocurrency market and the green conventional market, utilizing daily data. The research period covers 1 July 2020 to 30 September 2024. Employing the time-varying parametric vector autoregression (TVP-VAR) model and wavelet coherence analysis, we capture both short- and long-term spillovers across markets. The results show that cryptocurrencies, particularly Binance and Litecoin, act as dominant transmitters of volatility and return shocks, while green conventional indices function mainly as receivers with strong self-dependence. Spillover intensity is highly time-varying, with peaks during periods of systemic stress, particularly during the COVID-19 pandemic, and troughs indicating diversification opportunities. These findings advance the literature on systemic risk and portfolio design by showing that crypto assets can simultaneously amplify vulnerabilities and enhance diversification when combined with green finance instruments. For policy, the results highlight the need for regulatory frameworks that integrate sustainability taxonomies, mandate environmental disclosures for digital assets, and incentivize energy-efficient blockchain adoption to align crypto markets with sustainable finance objectives. This research enhances our understanding of the interrelationship between green investments and cryptocurrencies, providing valuable insights for investors and policymakers on risk management and diversification strategies in an increasingly sustainable financial landscape.

This study analyzes the role of green bonds in portfolio diversification and green bonds can help reduce risk without sacrificing expected returns. The data used are historical data on stock prices and green bonds traded on the IDX for a period of 7 years, namely from 2018 to 2024. The analysis method used is descriptive analysis, t-student test, ANNOVA test and multiple linear regression. The results of the study show that green bonds play an important role in portfolio diversification. As an instrument that has lower volatility compared to stocks, green bonds allow investors to reduce risk without sacrificing significant returns.

This study investigates whether green bond issuance in the presence of environmental, social and governance (ESG) performance improvements affects banks' financial performance. Using a panel of 1738 bank‐year observations from 2009 to 2023, the study examines the efficacy of ESG pillars in enhancing the value of green bond issuance, particularly assessing its impact on profitability, funding costs and loan portfolio quality. The findings suggest that green bond issuance enhances the quality of banks' loans, particularly in the presence of increased environmental performance. Moreover, an enhancement in governance scores decreases the funding costs in the presence of green bond issuance, which signals heightened investor confidence and improved reliability. This study contributes to the literature on sustainable finance by indicating the extent to which green finance can expand the financial and risk‐related benefits of green bond issuance by banks. Moreover, the findings indicate the strategic value of incorporating ESG factors into capital market operations for bank managers. This study empirically demonstrates that ESG and green bond issues are effective in improving financial performance and achieving long‐term sustainable development goals in the banking industry.

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The primary objective of this research is to investigate the two-fold impacts of portfolio Green Financing (GF) and GRI-based Sustainability Reporting Disclosure (SRD) on the financial performance of Indonesian banks. At the focal points of the country’s sustainability transition, banks play a catalytical role in directing capital between environment protection, climate risk policy, societal impact, industry adaptation, and long-term financial resilience. Using a panel data set of 44 IDX-listed commercial banks from 2021-2023, the research applies a dual-lens empirical framework: Tobin’s Q to measure market perception and Return on Risk Weighted Assets (RoRWA) to capture internal regulatory-aligned profitability. The result reveals that Green Finance and Sustainability Reporting Disclosure consistently improved RoRWA, confirming the strategic financial merit of green lending. Nonetheless, Tobin’s Q revealed that GF does not have a substantial effect, suggesting that the market may undervalue banks' sustainable business initiatives. SRD initially demonstrates significance but loses its explanatory power when the full model is introduced, indicating immaturity and narrative-heavy disclosure, which lack integrated rigorous financial materiality. Research emphasizes the importance of aligning SRD transparency and GF execution to accelerate new taxonomy-based reporting, develop RoRWA-linked ESG metrics, and explore potential macro and micro-prudential incentives. This research provides policy and managerial insight to support the scalability of green finance and credible sustainability reporting in Indonesia and other emerging markets.

Purpose : This study investigated the spillover effect and dynamic interconnectedness among green, clean, and sustainable markets. It identified the markets that are key transmitters and receivers, and it offered insights for investors and policymakers to optimize portfolio strategies and regulatory measures. Design/Methodology/Approach : The study employed the DY and BK models to analyze time frequency spillovers. Using the daily data, spillover was assessed across three time horizons that is short term (1–4 days), medium term (4–10 days), and long term (more than 10 days) to understand risk transmission. Findings : The results revealed that clean energy markets were the strongest transmitters of shocks, particularly in the medium and long terms; whereas, sustainable markets came out to be the largest receiver of shocks, with the highest vulnerability in the long term. Green Bonds (GB) and Carbon markets exhibited moderate spillover. Short-term spillover offered diversification opportunities; however, long-term spillovers were significant for risk mitigation. Practical Implication : Investors are recommended to balance their exposure between high-transmitting (clean energy) and high-receiving (sustainable market). This helps to optimize the risk-adjusted returns. GB are suitable for hedging as short-term spillovers offer better diversification opportunities. Originality/Value : The study contributed to existing financial research by analyzing volatility spillover among green, clean energy, and sustainable markets. Markets choose and use advanced econometric tools to provide a time–frequency-based risk assessment to investors and policymakers.

Cryptocurrency (crypto) markets have changed the investment landscape for many; however, they have started feeling the heat of the growing climate change awareness and are being prompted to shift towards green financial assets or clean/green cryptos. Climate change and sustainability have become an essential part of every discussion for businesses and investors. This study attempts to discover the connectedness between green financial assets and green cryptos. This paper builds upon a novel approach of copula analysis to shed light on the tail dependencies of the two asset classes. The findings provide interesting insights for investors to consider these two classes for portfolio diversification benefits or their hedging strategies, especially during a crisis period like COVID-19. The results indicate that the two asset classes exhibit distinct interdependencies, provide diversification and risk management opportunities, and behave differently during a crisis, and therefore present hedging and diversification opportunities to investors despite both asset classes being green.

The role of fintech in driving sustainable transactions and green investments around the world is increasingly important as the need for environmentally friendly financial solutions increases. Fintech does this by making products and services more accessible and encouraging wider participation in the green economy. Financial technology, or fintech, through inclusive and effective digital platforms helps more people access green financing. Fintech technologies help reduce carbon footprints by replacing resource-intensive conventional financial processes with digital-based technologies. In addition, fintech encourages green investments by offering services such as crowdlending for renewable energy projects, tokenization of environmentally friendly assets, and desire-based portfolio monitoring applications. Partnerships between fintech, regulators, and the conventional financial sector are essential to building an ecosystem that supports the transition to a low-carbon economy. This article discusses the strategic role of fintech in driving global green investments and sustainable transactions, the issues faced, and opportunities to achieve the sustainable development goals (SDGs) in the future.

Purpose The blue and green firms are notable contributors to sustainable development. Similar to other businesses in circular economies, blue and green firms also face financing constraints. This paper aims to assess whether blue and green lending help in optimizing the interest rate spreads and the likelihood of default. Design/methodology/approach This analysis is based on an unbalanced panel of banks from 20 eurozone countries for eleven years between 2012 and 2022. The key indicators of banking include interest rate spread and a market-based probability of default. The paper assesses how these indicators are influenced by exposure to green and blue firms after controlling for several exogenous factors. Findings The results show a positive relationship between green and blue lending and spread, while there is a negative link with the probability of default. This confirms that the blue and green exposure positively supports the credit portfolio both in terms of profitability and risk management. Originality/value The banking system is among the key contributors to corporate finance and to enable continuous access to sustainable finance, the banking firms must be incentivized. While many studies analyze the impact of green lending, to the best of the authors’ knowledge, this study is among the very few that extend this analysis to blue economy firms.

The article is devoted to the study of investment attractiveness in bonds of companies embodying the policy of responsible finance. The analysis of literature sources showed a statistically significant excess of rate of return for instruments issued by companies implementing ESG principles compared with issuers that do not develop their strategies according to sustainable finance paradigm. The formed bond portfolio based on the instruments of the sustainable development sector of the Moscow Stock Exchange has showed a relatively high yield and independence from fluctuations in interest rates of financial market due to completed immunization. Thus, investors can find solutions in the Russian market by investing in «green» bonds that are interesting from the riskreturn point of view.

Since the emergence of blockchain technology, several digital assets such as cryptocurrencies, DeFi, and NFTs have gained considerable attention from investors and policymakers. However, the blockchain market has significant negative ramifications for the environment that may transmit shocks towards eco-friendly financial assets. We use the rolling window wavelet correlation (RWWC) model and the quantile-based time-varying (QVAR) connectedness framework to analyze the dynamic price correlation and connectedness between the blockchain market and green (eco-friendly) financial assets. As a representative of the blockchain market, we use the price returns of four cryptocurrencies, DeFi, and NFTs. For green equities, we use the MSCI Global Environment Price Index and the S&P Green Bond Price Index. We find a low correlation between the blockchain market and green financial assets before the outbreak of COVID-19 and a strong correlation during the COVID-19 and the Russia-Ukraine war. The quantile VAR results show symmetric connectedness of the examined and identical spillovers between extremely positive and strongly negative returns. Green bonds and stocks are the system's major shock receivers. The transmission network results imply major shock transmissions are driven by short-term frequency, whereas there is a lower transmission in the long-term. © 2023 Elsevier Inc.

The global financial system is undergoing a profound transformation, driven by the rapid advancement of Artificial Intelligence (AI) and the escalating urgency of climate change. AI technologies—including machine learning, deep learning, and natural language processing—are redefining financial processes by improving efficiency, risk assessment, and decision-making in areas such as algorithmic trading, ESG scoring, and portfolio management. Concurrently, green finance has gained momentum as capital flows increasingly align with environmental sustainability goals, leading to the proliferation of green financial instruments. This study explores the intersection of AI and green finance, with a particular focus on AI’s capacity to detect and mitigate green asset bubbles—instances where environmentally themed assets are overvalued due to speculative investment behavior. The research demonstrates that AI, through tools such as multi-scale confidence indicators and the Phillips–Shi–Yu (PSY) test, offers superior predictive accuracy over traditional econometric models by identifying complex market patterns and shifts in investor sentiment. Theoretically, the findings challenge the Efficient Market Hypothesis and expand agency and behavioral finance theories by illustrating AI’s role in reducing information asymmetry and interpreting market psychology. Practically, AI strengthens risk management frameworks, improves internal controls, and ensures more responsible allocation of sustainable capital. Policy implications include the urgent need for clear AI governance structures, explainable AI mandates, and integration of AI-based risk tools into financial regulation. Addressing concerns around algorithmic bias, privacy, and energy consumption is essential to ensure AI contributes meaningfully to both financial stability and sustainable development.

ABSTRACT This study explores the relationship between the green bond and Islamic sectoral markets in terms of downside risk. A new framework was developed using CAViaR and QVAR techniques to construct hedging and portfolio strategies. Results show higher levels of downside risk connectedness and spillover across different risk environments, with short-run connectedness outperforming long-run. The downside risk connectedness and spillover are time-varying, influenced by major events like the Shale Oil Revolution, US–China trade war, COVID-19 pandemic, and Russo-Ukrainian conflict. Green bond market indices of China, the European Union, the US, and the global market receive net shocks in moderate and higher downside risk environments across various frequencies. US and global green bonds exhibit net transmitter roles in a downside-risk environment. Islamic Sectors BM, OG, FIN, CG, and HC are shock transmitters, while TELE and UTL are shock receivers across different downside risk environments and frequencies. Net roles are CS, INDUS, and TECH, subject to the downside risk environment and frequencies.

This chapter analyzes institutional investors' investment in green bonds, focusing on sustainable development financing and portfolio optimization. The research aims to explore the risks and potentials of investing in green bonds by analyzing the regulatory framework, previous research, and market trends. Special attention is given to portfolio optimization through integrating bonds issued according to ESG standards using Markowitz's diversification model. Secondary data sources include the Climate Bonds Initiative and the SP U.S. Municipal Green Bond Index. The results show that the average returns of green municipal bonds are slightly lower compared to conventional municipal bonds in the period from mid-August 2016 to mid-July 2023, but this difference is not statistically significant. This indicates the absence of a green premium or discount for green bonds, aligning with the literature's stance that these bonds do not enjoy particular advantages or penalties in the market compared to conventional bonds. The integration of green bonds into investment portfolios demonstrates a positive impact on returns, risk management, and overall portfolio efficiency, providing significant insights for institutional investors.

Abstract This study examines the stock’s response to corporate green bond issuance announcements. Analyzing a dataset of 230 global corporate green bond issuers from 38 countries between 2013 and 2022 through an event study, the findings reveal a positive market reaction, especially within the non-financial corporate sector. Green bonds in this sector are primarily used to fund their own eco-friendly projects, signaling a commitment to environmental sustainability, and generating investor confidence. Variation in market reactions across countries is noted, with developed countries exhibiting a significantly more positive response. This suggests that environmental initiatives hold greater value in these regions, highlighting the alignment between sustainable practices and investor sentiment. These results emphasize the potential advantages of integrating green bonds and their environmental commitments into investment strategies, particularly for portfolio diversification and attracting investors seeking sustainable opportunities.

This study investigates the performance of a green‐linked portfolio as a safe haven and hedge against traditional assets during periods of market turmoil, such as the COVID‐19 pandemic and the Russia‐Ukraine war. Utilizing a dynamic conditional correlation (DCC)‐generalized autoregressive conditional heteroscedasticity (GARCH) approach, the study analyzes the portfolio's correlations with  global asse like SENSEX 30, BSE ESG 100, Gold, S&P Green Bond Index, and US 10‐year Treasury bonds, demonstrating its stability. Our findings demonstrate that the green‐linked portfolio outperforms both individual stocks and major indices as well as traditional safe havens like gold and US Treasury bonds. The portfolio exhibits superior risk‐adjusted returns and lower volatility, particularly during crisis periods. The portfolio's strong positive correlation with the S&P Green Bond Index highlights its alignment with the broader green market. Interestingly, with a 95% hedge effectiveness, it emerged as a viable safe haven investment, surpassing gold in stability during market uncertainties. Further, forecast matrix results and VAR analysis confirm the portfolio's potential as a diversifier and safe haven asset, characterized by significant positive returns. Additionally, the SAP‐LAP framework provides clarity to stakeholders on safe‐haven options within green markets. Moreover, while green bond markets are more mature in developed economies, our results suggest that green‐linked investments in emerging markets, such as India, offer a promising avenue for investors seeking both financial returns and environmental impact. Overall, the study provides empirical evidence supporting the inclusion of green‐linked assets in diversified portfolios to enhance risk management and capture long‐term growth opportunities.

This study explores the incorporation of climate change into fixed income investment. We investigate the cost of decarbonization and the selection of Sustainable Investment strategies in portfolio construction, providing a comprehensive analytical framework. Employing a passive management style and through empirical analysis, we assess the tradeoff between decarbonization and the associated cost in terms of benchmark deviation for a corporate bond portfolio. We also propose an innovative strategy called “Green Parity,” which helps to improve traditional approaches to decarbonization. Our results challenge the common belief that pursuing decarbonization targets inevitably compromises risk-return outcomes.

The financial valuation of green bonds plays a pivotal role in supporting sustainability-focused energy investment portfolios and projects. As global efforts to combat climate change intensify, green bonds have emerged as critical instruments for financing renewable energy and other environmentally sustainable initiatives. This paper explores methodologies for valuing green bonds within sustainability-focused energy investment frameworks, emphasizing their financial, environmental, and social benefits. By leveraging financial modeling, risk assessment, and performance metrics, green bond valuation ensures optimal portfolio allocation while aligning with sustainability goals. Green bonds differ from traditional bonds by earmarking funds for green projects, including renewable energy generation, energy efficiency improvements, and carbon reduction initiatives. This study focuses on key valuation factors, including coupon rates, maturity periods, credit ratings, and market demand, while incorporating sustainability metrics such as environmental impact and alignment with the United Nations Sustainable Development Goals (SDGs). Advanced financial tools, including discounted cash flow analysis and Monte Carlo simulations, are utilized to model the risks and returns associated with green bond investments. Additionally, the paper addresses challenges in green bond valuation, such as the lack of standardization in reporting frameworks, potential greenwashing, and the evolving regulatory landscape. The importance of integrating Environmental, Social, and Governance (ESG) criteria into valuation methodologies is emphasized, highlighting how ESG-focused valuation enhances transparency and investor confidence. Case studies of successful green bond-funded energy projects are presented, demonstrating their positive environmental and financial outcomes. This research underscores the growing importance of green bonds in driving the transition to a low-carbon economy. By advancing robust valuation methodologies, stakeholders can effectively evaluate the financial and sustainability impact of green bonds, fostering greater investments in clean energy projects. The study also highlights the role of green bonds in achieving financial returns while addressing urgent climate challenges.

The green bond has emerged as an important financial instrument to advance environmentally friendly projects. While institutional investors have shown ample interest in green bonds, retail investors have lagged in their adoption. This study intends to examine the determinants of retail investors' attitude and intention toward green bond investment, utilizing the theory of planned behavior as the fundamental model and incorporating several context‐specific factors. Data from 506 Indian retail investors, representing the majority of Indian states and union territories (UTs), were collected through multi‐stage stratified random sampling. The PLS‐SEM method, coupled with artificial neural network (ANN) analysis and fuzzy set qualitative comparative analysis (fsQCA), was employed to test the hypothesized relationships, ensure the robustness of outcomes, and derive important practical insights. The findings suggest that intrinsic factors (perceived behavioral control and attitude) are superior predictors of investors' behavioral intentions relative to external factors (government policy support and social influence). Further, for green bonds to receive favorable evaluations from investors, they should display adequate financial cum environmental performance. Investors' attitude is significantly influenced by issuers' ratings and their willingness to pay premiums. Environmental concerns and perceived risk also influence investors' attitude toward green bonds, albeit with relatively lower strength. This study holds significance as it offers crucial implications for researchers, market participants, policymakers, and regulators involved in the development of the green bond market.

This paper examines the influence of green bond investments on bond fund performance in China from June 2016 to June 2023. We find that funds investing in green bonds (“green funds”) significantly outperform their non‐green counterparts. This outperformance can be attributed to a “redemption risk story,” where green bond investments mitigate redemption pressures, enabling fund managers to reduce liquidity buffers and reallocate capital to higher‐yielding assets. Consistent with this story, the outperformance of green funds is more pronounced during periods of heightened redemption risk. Our research contributes to understanding the benefits of socially responsible investments in bond mutual funds.

Purpose: This study examines retail investors' awareness, perceptions, and willingness to invest in green bonds within India's evolving sustainable finance landscape. It seeks to bridge the critical knowledge gap between policy-driven green finance initiatives and actual retail market participation by identifying key barriers and enablers influencing investment behaviour. The research provides empirical insights into how demographic factors, financial literacy, and market structures shape sustainable investment decisions in emerging economies. Methodology: The study employs a mixed-methods approach, combining quantitative analysis of survey responses from 100 retail investors with qualitative thematic analysis of 20 in-depth interviews. Data collection focused on four dimensions: awareness levels, risk-return perceptions, investment willingness, and structural barriers. Statistical analysis included descriptive metrics and correlation studies, while interview transcripts were coded using NVivo to identify recurring themes. The sample was stratified across age groups, education levels, and occupational categories to ensure representative insights. Findings: The results reveal moderate but uneven awareness, with higher recognition among older (46+ years) and more educated investors. Key findings include: (1) significant conceptual confusion regarding green bonds' environmental purpose, with many associating them incorrectly with infrastructure (27.6%) and education (25.0%); (2) equal weighting of financial returns (20.5%) and environmental impact (20.5%) in investment decisions; (3) pervasive trust deficits, with 43.6% expressing scepticism due to greenwashing concerns; and (4) structural barriers including limited accessibility and preference for traditional instruments. Notably, 41.6% of respondents demonstrated positive reception, suggesting untapped market potential conditional on policy interventions. Originality: This study contributes novel insights into sustainable finance behaviour in three key dimensions. First, it provides the first comprehensive assessment of India's retail green bond market, filling a critical gap in emerging market research. Second, it introduces an integrated framework analysing both cognitive barriers (trust deficits, knowledge gaps) and structural constraints (market access, liquidity). Third, the findings challenge conventional assumptions by demonstrating how Indian investors equally weigh financial and ethical considerations—a departure from dominant Western models emphasizing environmental values. The research offers evidence-based policy recommendations for accelerating retail participation in sustainable finance transitions.

No abstract available

The notable expansion of the green equity market has opened up new avenues for investment for market participants. This study looks at return connectedness, and the implications for portfolio management of green sector equities, and compares the performance of green and traditional investments. To achieve the research objectives, this study uses the TVP-VAR model along with portfolio strategies such as minimum variance portfolio (MVP), minimum correlation portfolio (MCP), and minimum connectedness portfolio (MCoP). Results demonstrate that the energy efficiency sector leads all others in information spillover while the bio/clean fuels sector is the largest net information absorber. Overall, energy efficiency, water, recycling and green building are found to be closely connected sectors, whereas bio/clean fuels, healthy living, natural resources and advanced materials are the least integrated industries in the system. However, a dynamic analysis demonstrates that inter-sector connectedness is time-varying and event-dependent. The MVP approach excels in the full and pre-COVID-19 sample, whilst the MCoP outperforms other methods in the post-COVID-19 scenario. In general, the portfolio exercise shows that green portfolios outperformed commodities but underperformed conventional equity and cryptocurrency portfolios. In contrast, following the COVID-19 pandemic, green portfolios have shown a greater return performance than all other conventional portfolios. The findings not only provide valuable insights to investors and policymakers in the effective management of investments and the green equity market, but also aid the achievement of objectives of environmental policies such as SDGs and the Paris Agreement.

Amidst the green transformations around sustainable drives, organizations are striving to integrate green business strategies (GBSs) to enhance their financial viability. This research argues that green strategies promote organizational efficiency that, in turn, improves financial performance and channel investments. Checking the mediating role of organizational efficiency through process improvement, product improvement, and organizational innovation focuses on financial performance and investment decisions. The study further postulates the moderation of management control system on the links between GBS and organizational efficiency parameters. The data were gathered by using surveys of 552 firms’ managers and investors at the Shenzhen Stock Exchange, China. PLS-SEM was applied to check the psychometric properties and analyze the data. The results confirm that GBS improves organizational efficiency and financial performance, exerting significant mediation effects. The study finds that moderation helps transform the green business strategy into tangible financial goals by amplifying the positive impact of GBSs. The study enriches the understanding of GBSs, organizational efficiency and investment decisions. The study also lauds the integration of GBSs that effectively transform financial performance and investment decisions.

An evaluation of sustainable (green) mutual fundperformance versus traditional equity mutual funds takes placein the Indian context during the period from 2020 to 2024. Theresearch design uses a descriptive approach to analyze 10sustainable funds alongside 10 traditional funds through theperformance indicators of Sharpe Ratio, Jensen’s Alpha, Beta,Standard Deviation together with Compound Annual GrowthRate (CAGR). The risk-adjusted performance assessmentshows that sustainable funds produce better returns thantraditional funds as measured through higher Sharpe Ratiosand Jensen’s Alpha measures. The market sensitivity levels ofsustainable funds are lower because their Beta valuesdemonstrate reduced exposure to market fluctuations. Standarddeviations from ESG funds are slightly higher but the fundsdemonstrate competitive returns through their top performersincluding SBI Magnum ESG Fund and ICICI Prudential ESGFund with CAGRs above 11%. Sustainable investment fundscreate a secure foundation for investors who get access to solidfuture returns in emerging markets and thus present anappealing investment alternative. The research provides crucialimplications which benefit investors at all levels aiming toestablish sustainable finance operations alongside climateconscious investments in India’s capital markets.

This study aims to identify the causality between bank financial performance measured by Return on Assets (ROA), Return on Equity (ROE), Net Interest Margin (NIM), and Operating Expenses to Operating Income (BOPO) with Green Financing Portfolio and CO2 emissions in the transportation sector. This study uses descriptive quantitative research methods and content analysis of the Sustainability Report of Bank Mandiri, Indonesia for the period 2016 to 2022. In this study, we collected data from Bank Mandiri's financial statements which included information on ROA, ROE, NIM, and BOPO. In addition, we also collected CO2 emission data available from 2016 to 2022. The research sample is Bank Mandiri as one of the state-owned banks in Indonesia. We used purposive sampling technique to select samples that meet the inclusion criteria. The collected data was then analyzed using statistical methods to test the relationship between the variables involved, namely the bank's financial performance (ROA, ROE, NIM, and BOPO), Green Financing Portfolio, and CO2 emissions in the transportation sector. We use content analysis to illustrate the results of Bank Mandiri's financial statements in graphical form. The results of the analysis show that the increase in Green Bond that started in 2016 has a significant impact on the increase in fund allocation for Green Financing Portfolio. This indicates a positive causality between Green Bond and Green Financing Portfolio. In this context, the causality between Green Financing Portfolio and CO2 emissions can be explained through the influence of investment in green technology and sustainable practices. With significant funds allocated through the Green Financing Portfolio, companies and institutions can implement projects that aim to reduce CO2 emissions in the transportation sector. This means that the use of Green Bond as a sustainable funding source has the potential to reduce the negative impact of transportation on the environment.

As investors increasingly incorporate non-financial performance metrics into investment decisions, CSR has become valuable due to its implications for voluntary disclosures and third-party ratings. Building on this premise, our study examines how green-bond issuance signals environmental commitment and is associated with ESG performance and valuation. While other studies examine this association, we go a step further and identify the green-bond features which are associated with ESG ratings. Using the Bloomberg database, we downloaded corporate green-bond data for 2550 green bonds. We use signaling theory as the foundation of the study. We deploy regression to test the relationships. Our findings show that green-bond features are associated with enhanced environmental and ESG disclosure scores but not with reductions in CO2 emissions relative to sales. The findings show weak associations of ESG with green-bond features. Taken together, the results contradict ‘greenwashing’ claims. However, the findings confirm that companies effectively signal environmental commitment through green-bond issuance. These insights enhance the understanding of green bonds’ nature and dimensions while providing meaningful implications for corporate policy.

The Sustainable Development Goals of the United Nation and interest by investors in Environmental, Social and Governance (ESG) investment strategies have caused a rapid shift to the green or renewable energy sector, from traditional or gray (oil, gas, and coal) energy companies. In this study, we examine whether and to what extent, financially speaking, there is a price to pay for investing in renewable energy sector equity. Moreover, we seek to determine whether green investments can be considered a hedge during times of financial stress. We find that alphas from investments in a portfolio of gray (overall energy sector) stocks and versus a portfolio of renewable energy equities during an exogenous, non-financial shock-the COVID-19 pandemic-and during non-crisis periods did not differ statistically. However, the renewable energy index showed higher idiosyncratic volatility than the energy index, as expected. The results are robust to alternative model specifications. From a practical perspective, our results are informative in that they provide insights into the tradeoffs associated with renewable energy investments. In particular, risk-adjusted returns to a renewable energy portfolio may be affected by greater idiosyncratic risk.

No abstract available

This study evaluates the effectiveness of advanced quantitative techniques, Monte Carlo simulations, AI-driven models, and Genetic Algorithms in enhancing investment portfolio management beyond Traditional Modern Portfolio Theory limitations. Analysing financial data from 2014-2024, this study assessed performance using Sharpe Ratio, Value-at-Risk, and Conditional Value-at-Risk across various market scenarios including black swan events. Findings demonstrate that Genetic Algorithms achieved the highest risk-adjusted returns while minimizing volatility, AI-driven models provided superior adaptability to market fluctuations, and Monte Carlo simulations significantly improved risk assessment compared to traditional approaches. The integration of green bonds into AI-optimised portfolios successfully balanced financial performance with sustainability objectives, appealing to environmentally conscious investors. This research confirms that AI and Genetic Algorithm approaches consistently outperform traditional models in optimising risk-adjusted returns under volatile conditions. Portfolio managers should consider implementing hybrid quantitative approaches that combine AI-based decision-making with Monte Carlo stress testing to enhance investment resilience and strategic planning in dynamic financial environments.Copyright© 2025 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.Article’s history: Received 3rd of July, 2025; Revised 29th of July, 2025; Accepted 2nd of September, 2025; Available online: 30th of September, 2025. Published as article in the Volume XX, Fall, Issue 3(89), 2025.

This research examines the opportunities and challenges in the green bond market, highlighting the financial institutions' role, determinants of investment decisions, and investor challenges. Based on a structured questionnaire and response analysis of 131 participants using ANOVA and T-tests, the results show that awareness of financial initiatives greatly differs with knowledge, income, and education levels but not with gender. Higher-income, better-educated, and more aware individuals exhibit higher awareness and take into consideration more factors while investing in green bonds. Issues of greenwashing and market inefficiencies are felt more acutely by these segments. The research concludes that to develop the green bond market in a sustainable manner, financial institutions need to step up education efforts, enhance transparency, and enhance market access across all socio-economic segments.

This study investigates the challenges impeding the development of an effective green bond market in Vietnam and evaluates its potential to support the country’s transition from carbon-intensive industries to a greener economy, in line with its climate commitments. This study uses a qualitative research approach, conducting semi-structured interviews with 15 stakeholders representing different segments of Vietnam’s financial sector. Thematic analysis was carried out in three stages: initial coding, exploring connections between codes and developing three main themes: barriers for corporate issuers, investor attractiveness and the green bond market’s potential. The major barriers for corporate issuers include a lack of intermediaries, regulatory shortcomings, high issuance costs and an underdeveloped corporate bond market. From the investor perspective, green bonds are perceived as less attractive due to relatively low yields, high project risks, limited market awareness and a scarcity of investment options. Despite these challenges, the market holds significant potential, supported by Vietnam’s net-zero ambitions, government backing and continuous legal framework improvements. The study covers a sample weighted towards issuers, due to the limited public awareness of green investment. Future research could also include regulatory perspectives and use quantitative methods to expand upon these findings, especially as the market matures. Findings inform policymakers and practitioners, offering recommendations such as establishing intermediaries, developing a green taxonomy, legal reform, infrastructure enhancement and investor education. A stronger green bond market can support sustainable development, reduce emissions and promote long-term environmental and social wellbeing. This study contributes by addressing a research gap in Vietnam’s green bond market using a qualitative, interview-based method, which is uncommon in the existing literature. It offers real-world insights from a diverse range of market actors, enabling a multidimensional understanding of market dynamics.

The increasing awareness of global issues in the sustainable development goals (SDGs) agenda initiated by the UN, has given rise to green investment in the economic sector, especially in the financial aspect. Referring to capital activities aimed at projects that address global issues. In recent years, green investment practices have begun to increase both in terms of the number of debt securities from individuals and countries participating in supporting the realization of SDGs 2030. In the Indonesian Capital Market there is a new financial instrument called green bonds. Green Bonds are regulated through the Financial Services Authority Regulation (POJK) Number 60 of 2017, as is the authority held by OJK regarding the regulation of the financial services sector in Indonesia. This article will discuss the legal status of green bonds in Indonesia and whether green bonds can be one of the financing schemes in Indonesia. However, interest in green bonds is still quite low considering the obstacles that result in investment risks. The research method with a normative juridical type, using literature and descriptive studies in solving problems, this research is expected to not only answer the risks but also the utility of green bonds in realizing economic and environmentally sustainable development goals.

Green bonds have become an essential financial tool for promoting sustainability by channelling funds into environmentally friendly projects. With the increasing focus on climate change and environmental issues worldwide, green bonds provide a reliable way to finance initiatives in renewable energy, sustainable infrastructure, and climate resilience. These bonds are in line with global sustainability goals, such as the United Nations Sustainable Development Goals (SDGs), and they give investors a chance to incorporate Environmental, Social, and Governance (ESG) criteria into their investment strategies. This study explores the role of green bonds in enhancing financial sustainability, assessing their effects on economic growth, risk management, and environmental advantages. It also compares the financial performance of green bonds with that of traditional bonds, taking into account their risk-return dynamics, investor interest, and market perceptions. Additionally, this study highlights significant challenges, such as the risks of greenwashing, the absence of standardization, and regulatory inconsistencies, which could impede the broader acceptance of green bonds.

Financial crises, climate change, the CoViD-19 pandemic and, more recently, the emerging political crisis in Eastern European countries, have created an extremely complex context, characterised by strong instability, which threatens the achievement of most of the goals set by the UN 2030 Agenda. In this context, we aim to synthesize the most significant passages of international positions, which have gradually consolidated from environmental issues in order to explore how the integration of ESG principles influences investment strategies and asset management in financial institutions, as well as the evolution and dynamics of the green bond market. The study highlights the importance of green bonds as key financial instruments for promoting sustainable investment, providing valuable insights for practitioners and academics interested in sustainable finance. The findings explore the leverage points for the progressive expansion of the ESG galaxy, which is a cross-cutting and shared topic with public procurement and the real estate market.

Concern on the detrimental effects of climate change is one reason for the increased global attention to sustainability issues. Significant investment is required to achieve carbon neutrality by 2050, and financing through green bonds is one alternative to fund green projects. On the other side, ESG criteria are frequently used as a measurement to assess corporations' commitment and actions toward net zero emission targets. However, whether the issuance of green bonds and ESG performance can increase profitability is still a big concern for companies. Using panel data from listed firms in China, South Korea, and Thailand from 2016 to 2022, this study employs the Difference-in-Difference (DID) model to investigate the impact of green bond issuance and ESG performance on firm profitability. Furthermore, panel data regression is used to examine the impact of green bond issuance on ESG performance. According to the findings of this study, the issuance of green bonds had no significant impact on ROA or ROE. However, according to the DID regression result, ESG performance (esg_1t) had a positive and significant effect on ROE (at a 10% significance level). This study also discovered that issuing green bonds (green_bond) had a negative and significant impact on the company's ESG performance.

Environmental, Social, and Governance (ESG) investing has gained unprecedented momentum in global financial markets, driving the need for sophisticated analytical frameworks that can process vast amounts of unstructured information. This research presents a comprehensive investigation into the application of natural language processing techniques for ESG news sentiment analysis and its subsequent impact on investment portfolio performance. The study develops a multi-dimensional sentiment analysis model that extracts ESG-related information from financial news sources, incorporating advanced text mining algorithms to quantify sentiment scores across environmental, social, and governance dimensions. Through empirical analysis of portfolio performance metrics, the research demonstrates that ESG sentiment-driven investment strategies yield superior risk-adjusted returns compared to traditional approaches. The methodology integrates real-time news processing capabilities with portfolio optimization algorithms, enabling dynamic allocation decisions based on sentiment-derived ESG signals. Experimental results indicate a 50.8% improvement in Sharpe ratio and 17.3% reduction in portfolio volatility when incorporating ESG sentiment analysis. The findings contribute to the advancement of sustainable finance technology and provide practical insights for institutional investors seeking to enhance portfolio performance through alternative data integration.

This article analyzes the effect of environmental, social, and governance (ESG) criteria on the choice and performance of financial portfolios. The study focuses on a sample of nine French industrial companies from the CAC 40, over the period from October 2016 to December 2022, divided into two sub-periods (before and during the crisis) using the k-means method. Three portfolios were constructed using genetic algorithms, according to the companies' ESG score level (high, medium, low), then compared using the stochastic dominance approach. The results show that before the crisis, portfolios with a high ESG score dominate others according to second- and third order. However, this dominance disappears during the crisis, highlighting the sensitivity of ESG performance to market conditions.

Environmental, Social, and Governance (ESG) practices have become an essential criterion for firm evaluation. While there is still no consensus on whether higher ESG scores are associated with better firm financial performance, ESG-conscious investors do screen their potential holdings for ESG performance. Investors can use negative or positive screening or a combination of the two. We examine which screening strategy is preferred by investors by examining flows to ESG mutual funds with positive, combined, and negative ESG screens. We find that positive screening has a positive association with dollar net fund flows relative to negative screening but no statistical difference with net flows as a percentage of total net assets. The result holds with control for the portfolio's ESG performance and disclosure scores. The net raw returns and 3-factor alphas of ESG mutual funds are not statistically different across funds with varying screening criteria. However, controlling for ESG performance scores and their E, S, and G pillars indicates that the negative screen strategy outperforms positive and, more so, combined screen portfolios. Controlling for ESG disclosure scores and their E, S, and G pillars shows better net raw returns for negative screen strategy relative to positive and combined screen portfolios, but not for alphas. This result is inconsistent with the behavioral/ethical explanation of investors' decisions but is more in line with modern portfolio theory.

There is a growing awareness of the need to integrate non-financial information arising from environmental, social, and governance (ESG) factors into corporate strategies, processes, and credit risk assessment to generate long-term value. Our paper aims to develop, through a Data Envelopment Analysis (DEA)-based approach, a credit risk assessment tool that could be used by banks in constructing an efficient and sustainable investment portfolio, able to maximize banks’ probability contemporaneously minimizing corporate inefficiency. This study was carried out on a sample of publicly traded energy companies in Europe, with the energy sector being highly environmentally sensitive. Our portfolio selection model proves to be a valuable tool for building an efficient and sustainable investment portfolio because it leads, within a budget constraint, to selecting both the most efficient companies in absolute terms and those for which ESG scores significantly improve corporate financial efficiency. Additionally, our results show that ESG ratings at high or low levels do not affect overall company efficiency, but at a middle level, they increase it. Findings contribute (and provide suggestions) to policymakers, credit risk managers, and academics.

ESG investment, which stands for environmental, social, and governance investing, has become an important strategy in the global financial markets, and its applications are becoming more relevant in developing nations. This study investigates the impact of environmental, social, and governance (ESG) integration on portfolio performance in emerging markets. The study aims to accomplish three primary objectives: evaluating the performance of ESG-compliant portfolios in comparison to non-ESG portfolios; determining whether ESG factors enhance risk-adjusted returns; and identifying the challenges and opportunities associated with ESG investing. The study takes a quantitative approach, making use of secondary data from financial databases such as Bloomberg and MSCI, and covers a length of time ranging from five to ten years. The Sharpe Ratio, Jensen's Alpha, and portfolio volatility are some of the performance indicators that are studied for both ESG-compliant and non-ESG portfolios. It is clear from the results of the regression analysis that there is a positive and substantial association between ESG compliance scores and risk-adjusted returns, which highlights the significance of ESG integration in terms of financial advantages. According to the findings of a comparative investigation, ESG-compliant portfolios outperform their non-ESG counterparts in terms of returns and display reduced volatility, providing investors with better stability. But broad use in developing nations is hampered by obstacles such as uneven environmental, social, and governance (ESG) data and regulatory frameworks that are not yet fully created. In these locations, there is a substantial opportunity for environmental, social, and governance (ESG) investment to generate sustainable economic development. Opportunities in renewable energy and rising investor awareness suggest this possibility. The results highlight the twin advantages of financial success and sustainability, providing policymakers, investors, and fund managers with insights that may be put into action. Following the conclusion of the study, suggestions are made to improve environmental, social, and governance (ESG) adoption by means of standardized reporting and legislative changes. These recommendations pave the way for future research to resolve data discrepancies and investigate sector-specific ESG dynamics in developing countries.

In the context of the growing prominence of socially responsible investment, the debate over whether sustainable corporate practices translate into sustained shareholder value has intensified. As a key tool for aligning their investment portfolios with responsible/sustainable corporate practices, investors rely on listed companies’ Environmental, Social, and Governance (ESG) ratings. This study aims to investigate the long-term impact of ESG practices on the stock performance of listed companies. We perform a Q1 2000–Q1 2025 backtest to analyse the comparative performance of a Best-in-Class ESG portfolio, constructed by the top 30 listed companies with market capitalisations above USD 2 billion ranked by Morningstar Sustainalytics’ ESG Risk Ratings as of 31 March 2025 against the S&P 500 Total Return index. We found that ESG leaders exhibited superior risk-adjusted performance, outperforming the S&P 500 Total Return Index. The BiC portfolios achieved a substantially higher CAGR and Sharpe ratio, while maintaining maximum drawdowns that remained comparable to the benchmark S&P 500 Total Return index. We also found that ESG advantages were more pronounced in market downturns, with the Best-in-Class ESG portfolio showing better CAGR and Sortino ratios. The findings of this study demonstrate that responsible governance and management create benefits for all stakeholders, including investors, society and nature, in the broadest sense of these terms.

This study investigates the financial impact of Environmental, Social, and Governance (ESG) performance in the global Critical Raw Materials (CRM) sector. Using a sample of 25 publicly listed CRM firms from 2018 to 2024, companies were segmented into "Laggard," "Improver," and "Leader" portfolios based on ESG risk ratings. A multi-method framework was applied, combining multivariate panel regressions, time-series models (GARCH, VAR), and machine learning (LASSO) to disentangle the financial effects of the E, S, and G dimensions. The research challenges the monolithic view of ESG performance by testing for non-linear, portfolio-dependent relationships between sustainability indicators and stock returns in a strategically vital industry. Results reveal a significant “ESG tug-of-war” among Improver firms: while markets reward social performance, they appear to penalize environmental and governance initiatives, likely due to transition-related costs. In contrast, ESG impacts for Laggard and Leader firms are statistically insignificant, suggesting either market indifference or fully priced-in performance. These findings offer a nuanced framework for understanding the conditional financial materiality of ESG. They have direct implications for investment strategies, sustainability reporting, and policy formulation in resource-intensive sectors navigating the energy transition.

Abstract This study compares the performance of five portfolios built according to the level of integration of environmental, social and governance values in the case of South Africa, over the period of four years from 2 January 2019 to 29 December 2022. The portfolios were built according to (1) the two dimensions of ESG ratings (responsible and irresponsible) and (2) the two levels of ESG implication (partially and significantly), and there is also a portfolio for non-engaged companies (no-reporting). Many recent studies comparing ESG and non-ESG portfolio performance have reported contradictory results so that this debate remains inconclusive. The main question I explore is whether portfolios integrating ESG values really matter in the case of a developing country with many economic and social challenges, as in the case of South Africa. For the purpose of the study, I have used four risk-adjusted measures (Sharpe ratio, Treynor ratio, Modigliani-Squared and Jensen’s alpha) for the performance evaluation. This study found an adverse effect of ESG on portfolio performance. Overall, the ESG Irresponsible portfolios achieved a better performance as compared to its counterparts. The study findings contribute to and enrich the academic literature by comparing the performance of five ESG portfolios in the South African context.

Pension funds are crucial in supporting environmentally sustainable and socially responsible investments. This paper focuses on an essential product of pension funds, target date funds (TDFs), constructing its portfolio model that considers environmental, social, and governance (ESG) investment. Our model utilizes the mean–variance–skewness–kurtosis optimization framework and accounts for the time‐varying relationship between realized higher moments and subsequent TDFs performance. A cardinality constraint is included to prevent over‐diversification and reduce costs, controlling the number of constituent funds in the TDFs. Since the model is multiobjective, we transform it into a single‐objective optimization through the investment manager's preferences. The numerical experiments represent the most suitable time‐varying higher moment strategy for the Chinese market and demonstrate the model's applicability to managers prioritizing risk over returns, which resonates well with the conservative investment of pension funds. We prove that controlling the number of constituent funds with cardinality constraint is advantageous for improving TDFs' performance. By selecting ESG funds as underlying assets, we highlight that green TDFs have better long‐term performance in terms of higher moment risk.

This article examines the risk-return dynamics of portfolios combining environmental, social, and governance (ESG) assets with traditional investment instruments. A hybrid optimisation framework is applied, uniting mean-variance principles with combinatorial selection and machine learning techniques. The study addresses two central questions: whether ESG funds provide diversification benefits, and whether they mitigate downside risk in periods of financial stress. The analysis draws on a dataset of five ESG and five non-ESG funds, spanning varied sectors and risk profiles, observed over a five-year horizon marked by diverse macroeconomic conditions. Portfolio performance is evaluated using the Sharpe ratio, with differential evolution and support vector machine algorithms employed to capture linear and non-linear aspects of risk-adjusted returns. The findings reveal a consistent positive association between ESG allocation and portfolio performance. Optimised portfolios frequently allocated 80-90 per cent of their weight to ESG assets, particularly GRID and ESGV. ESG holdings were shown to strengthen diversification, improve upside potential, and reduce downside exposure, especially during volatile market phases. Traditional assets contributed stability but played a weaker role in enhancing risk-adjusted returns. Statistical analysis confirmed both research hypotheses: portfolios integrating ESG investments achieved higher Sharpe ratios without excessive risk, and ESG funds demonstrated resilience under adverse conditions. Machine learning models further underscored the significance of non-linear patterns, which enhanced the explanatory power of ESG exposure in the optimisation process. In sum, the study contributes to growing evidence that ESG assets not only advance sustainability objectives but also deliver measurable financial benefits. The hybrid methodological approach illustrates the importance of balanced allocation constraints and robust optimisation in portfolio design. These results suggest that incorporating ESG assets can simultaneously reinforce financial stability and support long-term sustainable development.

In corporate strategy, ESG behaviors have emerged as a critical component for driving sustainable growth and enhancing overall enterprise performance. Functioning as a "green engine," ESG integrates principles of environmental stewardship, social responsibility, and strong governance, collectively contributing to a company's resilience and success. By integrating ESG considerations into operational models, companies can not only mitigate risks but also capitalize on new opportunities that arise from a rapidly changing global landscape. This paper examines the multifaceted impact of ESG behaviors on enterprise performance, providing a comprehensive understanding of how these practices can serve as a catalyst for growth within the corporate sector using qualitative research method. Findings reveal that ESG contributes to the long-term sustainable development of businesses by affecting green innovation, refining the investment portfolio, enhancing financial performance, bolstering customer relationships, and other comprehensive factors. The results suggest that companies that adopt ESG principles are better positioned to navigate the complexities of the global market and are likely to experience enhanced brand reputation and stakeholder engagement.

Investment funds pursuing environmentally sustainable portfolios use Environmental, Social, Governance (ESG) ratings as a basis for selection. However, ESG ratings are not standardized, and environmental ratings are scored only relative to peers within the same industry. The study introduces the Environmental Delta Score (EDS) designed to improve the assessment of environmental performance within investment funds. The EDS recalibrates traditional ESG metrics by incorporating industry group (IG) weighting, thus adding a layer of cross‐industry consideration. This is done by assigning a larger weight to IGs that have a greater environmental impact and higher potential to improve on this impact than IGs with a lower potential for improvement. We test the EDS in terms of portfolio construction and performance analysis on 11 Exchange Traded Funds (ETFs). Our data reveal an investor bias toward assets in industries with lower potential to create positive or reduce negative environmental impact. We propose a different portfolio selection with emphasis on environmentally impactful sectors. This highlights the importance of cross‐industry considerations currently still underdeveloped in the ESG landscape. The EDS thus offers a framework that can provide asset managers and investors with a tool to optimize portfolios toward better environmental performance without compromising financial returns. The EDS also offers insights for policymakers seeking to improve ESG rating frameworks and guide sustainable capital allocation.

Environmental considerations have gained prominence in investment portfolio decisions based on environmental, social, and governance (ESG) criteria. This shift is evident in reports such as the US Social Investment Forum Foundation’s 2020 Trends Report, which shows a surge in investments influenced by environmental considerations. For pension fiduciaries, however, the focus remains on the financial upliftment of beneficiaries. Fiduciaries must weigh the merits of socially responsible investments against their potential for financial return, with the Sharpe ratio being a critical measure. This article explores the relationship between ESG factors and traditional mean–variance portfolio performance by incorporating ESG criteria in stock return predictions and stock selections with techniques like weighted latent root regression and nonlinear regression models. The authors suggest that the environmental criteria can be one of the most important factors in risk–return trade-off and increase Sharpe ratio but are less effective than some other Kinder, Lyndenberg, and Domini (KLD) ESG factors in portfolio selection.

The green equity market has grown in tandem with environmental concerns, attracting increasing academic attention to best practices and the management of profitable portfolios in environmentally sustainable contexts. This study aims to identify the criteria and methods applied in the evaluation of low-carbon investment portfolios, while also presenting indicators from the main economic sectors and countries that have contributed to research in this field. A systematic literature review was conducted to analyze published studies on portfolio management in green contexts and their contributions to scientific knowledge. This approach enabled the identification of key criteria, methods, and the state of the art in this research domain. The primary criteria include performance indices of listed assets, combined with companies’ sustainability and transparency practices. The most prominent evaluation methods were mathematical, particularly statistical and econometric methods. Academic concern with managing low-carbon investment portfolios has intensified since 2010, consolidating in 2015 with the Paris Agreement and indicating a trend toward greater application of bibliometric analysis.

Since the worlds resources are running out due to global economic integration, low-carbon environmental protection and sustainable development are now the main forces behind global development and the unavoidable energy transformation process. China has publicly announced its double carbon strategic goals in the international community and has released macro measures to aid the growth of new energy enterprises. Against this background, BYD, as a benchmark in Chinas new energy vehicle industry, developing an investment strategy that minimizes investment risk is essential. This paper starts by constructing the listed company stocks suitable for BYD, and from the perspective of quantitative investment, establish the model and build the efficient frontier of the investment portfolio based on the Markowitz model, and obtain the minimum risk portfolio and the maximum Sharpe ratio combination to provide feasible and scientific quantitative investment advice for BYD. The company is still in its early stages of development. In terms of risk distribution, quantitative portfolio investment is just as sustainable as new energy and offers BYD a steady, systematic, and strategic profit model for the companys long-term growth.

The real estate sector must transition towards a low-carbon economy. In current investment decisions, carbon emissions are insufficiently considered and may not contribute to a low-carbon portfolio aligned with the sector's target. Therefore, investors require a change in the current DCF model-based investment decision to direct capital to projects that support this goal. This paper examines the impact of carbon accounting and pricing on a standard investment model using the Discounted Cash Flow (DCF) model. Three additional cash flows are modelled, representing the costs for Embodied Carbon (ECC), Operational Carbon Cost (OCC), and Maintenance Carbon Cost (MCC). This paper introduces a novel application of carbon pricing in real estate investment, accounting for embodied, operational, and maintenance-related emissions during the use phase, which results in a practical framework and guide for practitioners. The Carbon Price needs to be sufficiently high to make an impact and contribute to excluding energy-inefficient assets as an investment opportunity. Furthermore, the influence of ECC is minor compared to OCC, making carbon pricing for ECC less relevant in investment decisions. Ultimately, the MCC is a significant factor to consider when making an investment decision. Carbon pricing can encourage the use of circular and biobased materials, reducing emissions during the construction, renovation, and use phases. Investors should apply a carbon price to affect investment decisions by excluding carbon-intensive assets from investment portfolios. Investors could align their capital with the sector's low-carbon goal by including monetised carbon emissions in an investment decision.

Socially responsible investment (SRI) involves investors selecting companies based on their level of social responsibility. An eco-fund is an SRI-type investment trust that invests in environment-friendly companies, also known as “eco-excellent” companies. In many cases, the process of choosing the companies to be included in the trust is not transparent and, due to subjective decisions by fund managers, it is not clear how the investment rate is determined. In this study, we propose four eco-funds and evaluate their performance. Since the risk of the eco-fund should be distributed, the investment rate will be determined so that the percentage of the top brands is low. The portfolio should consist of brands with high efficiency in environmental investment. First, we evaluate a company’s environmental management capability and profitability using quantitative data such as the amount of sales, greenhouse gas emissions, and ROE (return on equity). We then determine a set of brands for the eco-funds to invest in. To determine the investment rate for each stock or portfolio, an environmental minimum variance frontier is calculated. The proposed eco-funds are (1) EEC fund, (2) Beta fund, (3) Expanded beta fund, and (4) Environmental index fund. (1) The EEC fund invests in brands that perform well both in terms of environmental management based on carbon productivity and profitability based on ROE. (2) The Beta fund invests in brands that perform well both in environmental management based on carbon productivity beta values and earnings efficiency based on ROE. (3) The Expanded beta fund invests in brands that perform well only in environmental management based on carbon productivity beta values; it includes more brands than the Beta fund. (4) The Environmental index fund invests in all the brands considered and is a benchmark to measure the performance of the other eco-funds.

The global transition to a low-carbon economy requires significant investment in the production of copper, a key mineral critical to electrification. The authors examine the disclosures of the 25 largest mining companies to compare the relative carbon intensities of major copper producers. They find that wide variation in business models, product mix, and stages of production kept in-house versus outsourced to the external value chain, combined with limited Scope 3 disclosures, render virtually meaningless a common metric of revenue-based carbon intensity that is used for portfolio construction. The authors demonstrate examples of inconsistencies and misleading disclosures that prevent investors from making sound capital allocation choices. They demonstrate that, for the copper sector, just 5 out of 15 Scope 3 categories are material for investor choices. The authors also highlight disclosures of an alternative metric of unit output carbon intensity, which a handful of issuers reveal. This metric, if available consistently for all issuers, can dramatically improve the investment decision relevance of carbon emissions intensity disclosures.

PurposeTo better understand the impact of choosing a carbon data provider for the estimated portfolio emissions across four asset classes. This is important, as prior literature has suggested that Environmental, Social and Governance scores across providers have low correlation.Design/methodology/approachThe authors compare carbon data from four data providers for developed and emerging equity markets and investment grade and high-yield corporate bond markets.FindingsData on scope 1 and scope 2 is similar across the four data providers, but for scope 3 differences can be substantial. Carbon emissions data has become more consistent across providers over time.Research limitations/implicationsThe authors examine the impact of different carbon data providers at the asset class level. Portfolios that invest only in a subset of the asset class may be affected differently. Because “true” carbon emissions are not known, the authors cannot investigate which provider has the most accurate carbon data.Practical implicationsThe impact of choosing a carbon data provider is limited for scope 1 and scope 2 data for equity markets. Differences are larger for corporate bonds and scope 3 emissions.Originality/valueThe authors compare carbon accounting metrics on scopes 1, 2 and 3 of corporate greenhouse gas emissions carbon data from multiple providers for developed and emerging equity and investment grade and high yield investment portfolios. Moreover, the authors show the impact of filling missing data points, which is especially relevant for corporate bond markets, where data coverage tends to be lower.

The high correlation between carbon-risk scores (CRS) and mutual fund style or sector suggests that investors picking funds with low carbon risk are simply picking funds with certain style or sector characteristics. As such, investors picking funds with a goal of reducing carbon risk may be increasing diversification risk. Moreover, investors choosing to invest in particular funds for their style or sector characteristics might be viewed as responding to carbon risk in a particular way, when they may not have considered carbon risk at all. Investors need more information about carbon-risk metrics so they can use them in conjunction with other metrics typically used in portfolio construction or investment management. Investors also need CRS for more funds, with scores made available by fund type—not just overall—to be able to use the scores with other metrics.

The transition to a low-carbon economy has placed financial institutions at the forefront of climate action, with banks playing a critical role in decarbonizing the global economy. The rise of net-zero banking represents a transformative shift in financial decision-making, aligning capital flows with sustainability goals to mitigate climate risks. As climate change poses systemic threats to economic stability, banks are increasingly committing to net-zero emissions by integrating environmental, social and governance (ESG) considerations into their lending, investment and operational strategies. The adoption of net-zero banking is driven by regulatory frameworks, investor pressure and stakeholder expectations, compelling financial institutions to reassess their risk models and portfolio exposures to carbon-intensive industries. Banks worldwide are setting ambitious targets to decarbonize their loan books and investment portfolios, with initiatives such as the Net-Zero Banking Alliance (NZBA). A critical aspect of net-zero banking is the reallocation of capital towards sustainable investments, including renewable energy, clean technologies and climate-resilient infrastructure. As banks continue to play a cardinal role in shaping the global economy, their commitment to decarbonization will be instrumental in achieving the broader objectives of the Paris Agreement and fostering a resilient, low-carbon future. Thus, the paper is an attempt to explore the rise of net zero banking and its global implications.

Where once screening-based or exclusionary approaches were the primary approach, investors are now building portfolios with more precise climate objectives. Recently, there has been a shift in interest toward portfolios that align to science-based temperature targets. This entails going beyond current carbon emissions to modeling how companies will tackle climate risk in the future, how they are evolving their product mix and supply chains, whether they are investing in green technologies, and so forth. These forward-looking metrics are conceptually different from standard carbon emissions measures, and include physical and transition risk scores or value-at-risk measures, climate risk ratings, implied temperature ratings, and more. Here, we summarize the features of forward-looking climate data, discuss how they differ from traditional carbon emissions, and highlight the potential issues to integrating them in investment strategies. A portfolio that is forward looking can be very different from a backward-looking low-carbon portfolio in terms of sector composition and securities held.

The regulatory focus on quantifiable sustainable investing shifts investors’ demand toward impact products, which creates challenges in achieving their primary target of outperformance. This study demonstrates the implications of sustainable investment in actively managed credit portfolios using carbon emissions, Sustainable Development Goals (SDGs), and green bonds. All three measures exhibit a low correlation with systematic factors, such as value and momentum, providing an opportunity for a sustainable alpha. Furthermore, we demonstrate a concave relationship between outperformance and sustainability. Therefore, systematic investors achieve a sustainable portfolio at a low cost, whereas sustainability-oriented investors harness factor returns and meet their initial targets.

Purpose While an increasing number of investors value socially responsible investment practices, Bitcoin has faced criticism for its carbon footprint resulting from excessive mining power consumption. By examining Bitcoin’s interconnectedness with environmental, social and governance (ESG) equities, this study aims to construct a socially responsible investment strategy for cypto investors. Design/methodology/approach This study uses wavelet analysis and a time-varying parameter vector autoregressive (TVP-VAR) model to uncover the interdependence between ESG equities and Bitcoin. This study computes the optimal ratio, showing that Bitcoin significantly reduces portfolio risk when combined with green stocks. Findings The results show that co-movements between green stocks and Bitcoin are low, indicating that they are suitable combinations for portfolio diversification. From an environmental perspective, this investment strategy offers a theoretical solution to mitigate the negative impacts associated with Bitcoin mining. It aims to address the dilemma faced by sustainability-conscious investors, who must navigate the economic payoff of Bitcoin against their commitment to green investment principles. Practical implications The findings can provide valuable insights for policymakers seeking to develop strategies that promote sustainable investments among crypto investors. Originality/value Research on ethical investment practices in the cryptocurrency market remains in the early stages of development. Ethical investors can benefit from including Bitcoin in their ESG equity portfolios.

Electric vehicle, as one of the low carbon footprints, has transformed a wide range of energy and public sectors. Utilizing the WHO Covid-19 pandemic statement, we search for the optimal portfolio from the top seven Electric Vehicle (EV) enterprises in ASEAN. Using the MonteCarlo simulation of optimal risky assets portfolio, we find that before and during the pandemic, the optimal portfolio weights differ significantly. Before (during) the pandemic, the investment weight of optimal portfolio consists of Toyota Motor Corporation 49.83% (42.99%), Star 8 Corporation 23.20% (9.73%), Hyundai Motor Corporation 20.81% (10.63%), BMW.DE 2.79% (32.08%), Honda Motor Company 2.56% (0.64%), Mitsubishi Motor Corporation 0.68% (0.09%), and Nissan Motor Co. 0.14% (3.84%). The Sharpe Ratio shows how during the pandemic, the portfolio of EV stocks give more excessive return compare to their risks, from 93.55% to 250.62%. These findings support the notion of how EV stocks are investible, especially during the Covid-19 pandemic.

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Carbon emissions are recognized as major contributors to the greenhouse effect, accelerating global warming. Meanwhile, technology innovation is acknowledged as a key determinant influencing carbon emissions. Therefore, implementing low‐carbon technology innovation (LCTI) becomes a critical approach to mitigating global warming. However, the uncertainty of LCTI and capital constraints hinder enterprises from adopting such innovations. Additionally, the constraints imposed by traditional financing channels exacerbate the predicament faced by enterprises. In this paper, we build a two‐echelon supply chain model in the context of LCTI uncertainty, consisting of a retailer with sufficient capital and a manufacturer facing capital constraints, where traditional financing channels are restricted, to explore the strategies of LCTI investments, carbon asset financing, and product pricing. The research findings are as follows: Firstly, LCTI does not consistently result in higher expected profits for the manufacturer. This means that the manufacturer may lack the willingness to invest in such endeavors unless the probability coefficient of success in LCTI surpasses a certain threshold. Conversely, the retailer always benefits as a free rider from the manufacturer's investment in low‐carbon technology. Secondly, both carbon asset pledge and sell–buyback financing can continuously increase the manufacturer's LCTI investments, but their impacts on the manufacturer's profits differ. Under pledge financing, higher LCTI investments imply greater profits for the manufacturer. In contrast, under sell–buyback financing, the impact may be either positive or negative, which depends on the fluctuation of carbon price. If carbon price escalates beyond a certain degree, sell–buyback financing may backfire. Yet, it also provides the opportunities for the manufacturer to maximize profits. Thirdly, improving LCTI investments can either benefit or harm the environment. This is contingent upon the relationship between the carbon emission ratio and the demand ratio. Win–win economic benefits and environmental performance can be achieved only when the carbon emission ratio is lower than the demand ratio. Finally, in the context of LCTI uncertainty, although carbon asset financing can enhance the manufacturer's LCTI investments and has the potential to improve the manufacturer's profits, it fails to prevent the retailer's free‐riding behavior. An LCTI cost‐sharing mechanism can limit such behavior and help the manufacturer and retailer reach a win–win outcome.

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This study investigates optimal capital allocation strategies for corporate decarbonization initiatives under technological transition constraints. A decision analysis framework grounded in real options theory is developed, incorporating fuzzy-set parameters to address implementation uncertainties in emission mitigation systems. Analytical results demonstrate inverse correlations between critical intervention factors (technological decarbonization efficiency, environmental taxation levels, eco-product market premiums, and fiscal incentive mechanisms) and capital deployment thresholds. Improved technical specifications, reinforced regulatory constraints, positive consumer responses, and targeted subsidy mechanisms synergistically facilitate sustainable infrastructure investments. Comparative evaluations confirm the proposed fuzzy option model’s superiority over conventional NPV methods in valuing managerial flexibility and mitigating valuation biases. Sequential option analysis reveals that modular implementation approaches can generate incremental value through adaptive capacity in operational execution. Empirical validation through ‘’industrial case studies illustrate the framework’s practical efficacy in assessing sustainable technology portfolios, offering actionable insights for strategic planning in carbon-intensive industries. This research contributes methodological advancements for timing optimization and risk assessment in environmental technology adoption scenarios.

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This study addresses the dual challenge faced by modern investors: managing traditional portfolio risk while simultaneously mitigating emerging climate-related financial exposures. We conduct a comparative analysis of three green assets, green bonds (GB), clean energy (CE), and clean cryptocurrency (CCR), and their conventional counterparts to determine their effectiveness as hedges and safe havens for global equity portfolios. Using a multi-stage empirical investigation that includes volatility spillover analysis, formal hedge and safe-haven tests, and out-of-sample portfolio optimization, our findings clarify each asset's practical utility. While initial tests show no asset qualifies as a reliable hedge, and all exhibit some safe-haven potential, the portfolio analysis reveals that only green and conventional bonds consistently and substantially reduce both variance and downside tail risk. This outcome highlights that an asset's stable, low-volatility profile is a more powerful determinant of risk reduction in long-only portfolios than its correlation. Ultimately, this study concludes that Green Bonds uniquely fulfill the dual objective, delivering portfolio protection nearly identical to conventional bonds while also providing a direct mechanism for mitigating climate risk.

In sustainable portfolio management, categorizing assets as “brown“ or “green“ based solely on ESG ratings can be misleading. A positive ESG score does not inherently indicate environmental responsibility unless it is evaluated relative to a meaningful benchmark. We propose a rescaled ESG rating system that measures each asset’s environmental standing relative to a threshold set by policymakers, reflecting the urgency of the current climate crisis. In this system, assets are assigned positive scores if they exceed the threshold (green) and negative scores if they fall below it (brown), enhancing the interpretability of sustainability metrics in portfolio construction. However, a challenge arises when aggregating these scores into an overall portfolio rating. Under sustainable portfolio optimization developed in [11], short positions in brown assets, otherwise effectively betting against polluting companies, can paradoxically improve the portfolio’s sustainability score. This creates a misleading incentive structure. To address this, we introduce a constraint that prohibits short positions in brown assets, ensuring that such investments do not positively impact the portfolio’s sustainability rating. While this restriction better aligns with environmental objectives, it also introduces complexity into the optimization process. To resolve this, we present an intuitive algorithm inspired by the active set method, which we refer to as Green Portfolio Optimization, capable of handling these constraints efficiently even in high-dimensional settings.

This work looks for the optimal allocation of different assets, namely, the G7 stock indices, commodities (gold and WTI crude oil), cryptocurrencies (Bitcoin and Ripple), and S&P Green Bond, over four periods: before the COVID-19 crisis, during the COVID-19 crisis and before the Russia–Ukraine war, during the COVID-19 crisis and Russia–Ukraine war, and after the COVID-19 pandemic and during the Russia–Ukraine war. Metaheuristics, Non-dominated Sorting Genetic Algorithm (NSGAII), Strength Pareto Evolutionary Algorithm (SPEA2), and Particle Swarm Optimization (PSO) are applied to find the best allocation. The results reveal that there a significant preference for the S&P Green Bond during the four periods of study according to three algorithms, thanks to its portfolio diversification abilities. During the COVID-19 pandemic and the geopolitical crisis, the most optimal portfolio was Nikkei 225 because of its quick recovery from the pandemic and poor reliance on the Russia–Ukraine markets, while WTI crude oil and both dirty and clean cryptocurrencies were poor contributors to the investment portfolio because these assets are sensitive to geopolitical problems. After the end of the pandemic and during the ongoing Russia–Ukraine war, the three algorithms obtained remarkably different results: the NSGAII portfolio was invested in various assets, 32% of the SPEA2 portfolio was allocated to the S&P Green Bond, and half of the PSO portfolio was allocated to the S&P Green Bond too. This may be due to changes in investors’ preferences to protect their fortune and to diversify their portfolio during the war. From a risk-averse perspective, NSGAII does not underestimate the risk, while in terms of forecasting accuracy, PSO is an adequate algorithm. In terms of time, NSGAII is the fastest algorithm, while SPEA2 requires more time than the NSGAII and PSO algorithms. Our results have important implications for both investors and risk managers in terms of portfolio and risk management decisions, and they highlight the factors that influence investment choices during health and geopolitical crises.

This article aims to investigate the impact of sustainable assets on dynamic portfolio optimization under varying levels of investor risk aversion, particularly during turbulent market conditions. The analysis compares the performance of two portfolio types: (i) portfolios composed of non-sustainable assets such as fossil energy commodities and conventional equity indices, and (ii) mixed portfolios that combine non-sustainable and sustainable assets, including renewable energy, green bonds, and precious metals using advanced Deep Reinforcement Learning models (including TD3 and DDPG) based on risk and transaction cost- sensitive in portfolio optimization against the traditional Mean-Variance model. Results show that incorporating clean and sustainable assets significantly enhances portfolio returns and reduces volatility across all risk aversion profiles. Moreover, the Deep Reinforcing Learning optimization models outperform classical MV optimization, and the RTC-LSTM-TD3 optimization strategy outperforms all others. The RTC-LSTM-TD3 optimization achieves an annual return of 24.18% and a Sharpe ratio of 2.91 in mixed portfolios (sustainable and non-sustainable assets) under low risk aversion (λ = 0.005), compared to a return of only 8.73% and a Sharpe ratio of 0.67 in portfolios excluding sustainable assets. To the best of the authors’ knowledge, this is the first study that employs the DRL framework integrating risk sensitivity and transaction costs to evaluate the diversification benefits of sustainable assets. Findings offer important implications for portfolio managers to leverage the benefits of sustainable diversification, and for policymakers to encourage the integration of sustainable assets, while addressing fiduciary responsibilities.

The blistering emergence of the Financial Technology (FinTech) provided the opportunity to alter the traditional priorities of investments to use the information-based decisions made in accordance with the advanced machine learning (ML) algorithms. On the same note, the world financial industry is under pressure, and it is underwriting pressure to be sustainable and environmentally friendly in their investments. This article presents a framework of sustainable finance using machine learning, which is related to the development of green portfolios, the assessment of carbon risks, and the ethics-driven optimization of finance. It is suggested that the supervised learning to classify assets, the reinforcement learning to optimally rebalance the portfolio, and the explainable artificial intelligence (XAI) to make decisions clear can be incorporated into one model. The empirical research of ESG (Environmental, Social, and Governance) data of MSCI and Refinitiv data use reveals that there is an increment in ratings of portfolio sustainability by 15 percent, and a growth in risk-adjusted returns by 11 percent over a base model. This is because the findings can be used to establish the extent to which ML-enabled FinTech applications can serve to support the realization of sustainable investment goals throughout the world and align financial growth with the green strategy.

The increasing focus on sustainability in financial markets has led to a heightened interest in understanding the interconnectedness of various asset classes, particularly those related to the blue and green economies. This study aims to measure the spillover relationships between four blue-economy indexes and four green-economy indexes alongside Bitcoin and Gold. We utilize an innovative quantile and frequency connectedness analysis to explore the interplay of spillover dynamics across these diverse financial markets. Our analysis is based on data covering the period from 10th October 2021 to 5th January 2024. Our findings reveal significant spillover effects among the selected indexes, indicating that both blue and green assets exhibit distinct yet interrelated behaviours in response to market changes. These results underscore the importance of integrating insights from both economies into investment strategies, offering valuable implications for risk management and portfolio optimization in an increasingly complex financial landscape.

This paper presents a study on the use of genetic algorithms (GAs) for portfolio optimization across diverse assets, including equities, cryptocurrencies, and ESG-focused instruments. The optimization framework incorporates real-world constraints such as full capital allocation, cardinality limits, and diversification bounds, with objectives centered on maximizing risk-adjusted returns like the Sharpe Ratio. The GA methodology is outlined with details on portfolio encoding, fitness evaluation, and genetic operators. Performance is compared against traditional mean-variance optimization and meta-heuristics like Particle Swarm Optimization and Ant Colony Optimization using historical market data. Results show that GAs consistently achieve superior risk-adjusted returns and demonstrate greater robustness under complex constraints, particularly in portfolios adhering to ESG criteria.

Despite a burgeoning literature in the sphere of cryptocurrencies and green assets, yet, as of date, the literature fares poorly in terms of a holistic assessment of all asset classes, let alone stress testing such global portfolio risk under various market conditions. Our paper fulfills such a gap in the literature. Findings reveal that, irrespective of bearish or bullish market phases, green assets should be incorporated to mute down portfolio tail risk. Robustness tests performed either via different distribution type or CVaR analysis do not materially alter the main findings of our paper. The confluence of two forces substantially boosts tail risk, namely, passive investment strategy coupled with a crypto-augmented base model. Overall, our paper advocates the inclusion of green assets not as a choice but as an obligation to portfolio managers in view of curtailing both VaR and CVaR risk levels, all geared towards hitting two birds with one stone-simultaneously buttressing a greener world while effectively mitigating global portfolio tail risk.

This study examines the financial performance of diversified portfolios composed of various asset categories, including green cryptocurrencies, non-green cryptocurrencies, energy cryptocurrencies, stocks of leading companies, stocks of top energy companies, and stocks of prominent sustainable companies within the context of G7 nations. Additionally, it investigates the financial performance of green and non-green cryptocurrency portfolios across these regions. It aims to compare returns while examining the initiatives undertaken by these countries to foster sustainable financial systems. The research also explores how investors can leverage portfolio optimization to enhance returns in the rapidly evolving digital currency market. The study employs two machine learning techniques. First, six constraints, including maximum Sharpe ratio, minimum variance, maximum return, Sortino ratio, and Black-Litterman model, were applied to build portfolios for green and non-green cryptocurrencies. The model started with an 80%-20% train-test separation to find suitable allocations that it improved using full dataset retraining. The results explained that the highest Sharpe ratio portfolio generated the finest performance in the U.S. and Japan because of their strong financial market institutions and active participation from institutions. The investment cultures of Canada and Italy led to their selection of minimum variance portfolios. The Black-Litterman model worked well in the UK to produce equilibrium between market expectations and real risk-returns while German investors chose maximum return portfolios due to their risk tolerance. The French financial industry put risk-adjusted returns at the forefront thus the optimized Sortino ratio strategy proved most appropriate. A comparison between green and non-green portfolios shows that green portfolios regularly exhibited lower volatility together with superior risk-adjusted returns especially when sustainability policies were clearly defined in the nation. The higher returns from non-green portfolios came alongside higher speculative risk which made them susceptible to market volatility. This study demonstrated that selecting portfolios should be done based on specific market features that vary from country to country. Those who need stable long-term returns can achieve it through green investing while investors with high tolerance for risks can spend in non-green investments. Future studies should concentrate on developing dynamic rebalancing methods for portfolios while integrating decentralized finance (DeFi) technology to optimize portfolio management systems.

Environmental, social, and governance (ESG) factors have become key factors in modern portfolio management, shaping how investors think and how they allocate their assets. At the same time, the presence of asymmetric and heavy‐tailed return distributions highlights the necessity of moving beyond the classical mean–variance (MV) framework by incorporating higher‐order moments, such as skewness and kurtosis, into portfolio optimization. To address this issue, we introduce a unified mean–variance‐skewness‐kurtosis‐ESG (MVSK‐ESG) optimization model. This model uses different ESG score thresholds and focuses on ESG leaders within the Dow Jones Industrial Average and the Nasdaq 100. This model incorporates ESG scores into the objective function using a difference‐of‐convex programming framework to address the model's inherent nonconvexity. Empirical results show that MVSK‐ESG portfolios consistently outperform traditional MV and ESG‐constrained portfolios as well as their benchmarks, with higher risk‐adjusted returns. The proposed framework provides a robust approach for integrating sustainability considerations into portfolio construction.

In mean-variance portfolio optimization, multi-index models often accelerate computation, reduce input requirements, facilitate understanding, and allow easy adjustment to changing conditions more effectively than full covariance matrix estimation in many situations. In this paper, we develop a multi-index model-based portfolio optimization approach that takes into account aspects of the environment, social responsibility and corporate governance (ESG). Investments in assets related to ESG have recently grown, attracting interest from both academic research and investment fund practice. Various literature strands in this area address the theoretical and empirical relation among return, risk and ESG. Our portfolio optimization approach is flexible enough to take these literature strands into account and does not require large-scale covariance matrix estimation. An extension of our approach even allows investors to empirically discriminate among the literature strands. A case study demonstrates the application of our portfolio optimization approach.

Abstract In recent decades, the rising challenges posed by climate change have prompted investors to take a keen interest in green assets and incorporate them into their portfolios to achieve optimal returns. Therefore, this article explores the static and dynamic connectedness between renewable energy stocks (solar, wind, and geothermal), green cryptocurrencies (Stellar, Nano, Cardona, and IOTA), and agricultural commodities (wheat, cocoa, coffee, corn, cotton, sugar, and soybean) using the TVP-VAR (time-varying parameter vector autoregression) framework offering novel empirical evidence for investors and portfolio managers. The connectedness is examined across two distinct sub-samples: during COVID-19 and post-COVID-19 times. Because the relevant connectedness can have implications for diversification benefits, we proceed with the computation of optimal weights, hedge ratios, and hedge effectiveness using the DCC-GARCH model. The main findings are as follows: We first find that green cryptocurrencies particularly Cardona and Stellar exhibit the highest spillovers to the network and wind energy stock has the least connectedness with the other markets. Second, the dynamic NET spillover indices reveal that cotton, cocoa, and coffee are consistently net receivers over the entire period except in the beginning of the pandemic. Third, renewable energy stocks exhibit diverse positions implying that the impact of the pandemic has varied significantly across the sectors. Finally, agricultural commodity depicts greater weights in the pandemic period under scoring the benefit of a diversified portfolio consisting of agriculture and green assets.

Presently, financial portfolio managers lack a solid basis for building a reliable risk management strategy for green debt instrument investments due to the lack of compelling growth and resilience data. Therefore, this study assesses the role of green bonds in financial markets by assessing and correlating their complex scaling behaviors across multiple periods with those of key benchmark assets (e.g., conventional bonds, high-yield bonds, Euro-Dollar exchange, Dow Jones Industrial Index, Bitcoin, and Gold). Specifically, we explore linear and nonlinear correlation patterns using cross-correlation tests and the dynamic conditional correlation model, focusing on bond interactions under various degrees of freedom. Our analysis reveals that although most assets exhibit nonlinear correlations, Bitcoin uniquely aligns linearly with U.S. bonds under certain conditions. Green bonds, however, display nonlinear correlations with Bitcoin and stand out for their distinct upward financial persistence. We find also that green bonds are primary drivers in the financial domain, highlighted by their pronounced interactions and the consistent cross-correlation with the Euro-Dollar exchange rate. Moreover, green bonds have the lowest multifractality, showing persistent upward trends and antipersistent downward trends, rendering them quite resilient during periods of high volatility. These results imply that green bonds may be advantageous to portfolio risk management strategies, especially during crises when diversification and hedging tactics are needed.

Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective. Traditionally, some form of mean-variance optimization is used with the aim of maximizing returns while minimizing risk, however, more recently, deep reinforcement learning formulations have been explored. Increasingly, investors have demonstrated an interest in incorporating ESG objectives when making investment decisions, and modifications to the classical mean-variance optimization framework have been developed. In this work, we study the use of deep reinforcement learning for responsible portfolio optimization, by incorporating ESG states and objectives, and provide comparisons against modified mean-variance approaches. Our results show that deep reinforcement learning policies can provide competitive performance against mean-variance approaches for responsible portfolio allocation across additive and multiplicative utility functions of financial and ESG responsibility objectives.

The study explores the theoretical foundations and practical dimensions of green finance, emphasizing its role in aligning financial systems with global sustainability goals. Green finance, through instruments such as green bonds, green equity, and sustainability-linked portfolios, has emerged as a transformative mechanism for channeling investments into environmentally responsible projects. The paper critically examines how these instruments not only support climate mitigation and low-carbon development but also yield competitive financial returns, thereby reshaping investor behavior and corporate accountability. A key focus is placed on the intersection between institutional frameworks and individual investment decisions, highlighting the growing significance of ethical banking, ESG funds, and personal financial choices in driving systemic change. The analysis further investigates challenges such as greenwashing, information asymmetry, lack of standardized ESG metrics, and uneven regional adoption that hinder the sector’s full potential. Drawing from diverse theoretical perspectives, the paper argues that the effectiveness of green finance depends on integrating financial innovation, regulatory frameworks, and behavioral insights into a coherent framework. Ultimately, green finance is positioned as both a financial innovation and a paradigm shift, bridging economic growth with ecological responsibility and serving as a critical enabler of the United Nations Sustainable Development Goals (SDGs).

No abstract available

As the issue of sustainable development is progressively valued, financial institutions increasingly incorporate ESG (Environmental, Social, and Governance) factors into portfolio construction to achieve risk management and return maximization. This study investigates how ESG factors influence stock investment returns and performance, while analyzing their implications for investment strategies. This study employs a two-way fixed effects model to analyze the relationship between ESG criteria and equity performance in Chinas stock market, identifying a meaningful and statistically robust positive association. The findings emphasize the material marginal effects of ESG factors, as incremental improvements in corporate ESG practices systematically translate into measurable financial gains. Notably, firms with superior ESG ratings exhibit persistent long-term outperformance relative to market benchmarks, suggesting that ESG integration serves as a robust predictor of sustainable value creation. This evidence underscores the growing economic relevance of ESG factors considerations in emerging equity markets. As ESG factors serve as critical indicators of corporate sustainability, ESG principles should be proactively incorporated into enterprises top-level strategic planning to achieve long-term value creation. Investors are advised to incorporate ESG standards into their strategic decision-making processes to enhance risk mitigation, achieve financial objectives, and incentivize enterprises to advance their ESG practices.

Abstract In this paper, we implement an integrated framework for constructing ESG-constrained, downside-risk-optimized equity portfolios in the European stock market. Extending traditional mean-variance approaches, we employ downside-oriented risk measures-conditional value at risk (CVaR) and semi-variance-to better capture investors’ asymmetric aversion to losses. ESG scores are introduced as binding constraints based on percentile thresholds, ensuring that portfolios comply with predefined sustainability standards. Semi-variance and CVaR objectives are formulated as convex programs to enable tractable optimization. Using data from Euro Stoxx 50 and Euronext 100 constituents, our empirical analysis reveals that: (i) integrating downside risk measures enhances tail-risk protection and may improve performance for loss-averse investors; but (ii) enforcing ESG constraints, particularly at stricter thresholds, leads to reduced diversification and a decline in risk-adjusted returns (e.g., Sharpe and Sortino ratios). These findings highlight the inherent trade-off between sustainability and financial efficiency, underscoring the importance of moderate ESG integration when balancing performance and ethical objectives.

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Leveraging ARMA–GJR–GARCH models with skewed Student-t innovations, extreme value theory, and dynamic R-vine copulas, the framework effectively captures volatility, tail risks, and evolving asset interdependencies. Utilizing daily data from 2014 to 2024, the models generate value-at-risk forecasts consistent with international standards such as Basel III’s 10-day 99% VaR and rolling Sharpe ratios for portfolios integrating green bonds compared to traditional asset allocations. The results demonstrate that green bonds, fixedincome instruments funding renewable energy and other environmental projects, significantly improve risk-adjusted returns and have the potential to reduce capital requirements, particularly for life insurers with long-term sustainability mandates. These findings underscore the importance of portfolio-level capital assessment and support the proactive integration of ESG considerations into supervisory investment guidelines to enhance financial resilience and align the insurance sector with Thailand’s sustainable finance agenda.

This study examines the diversification and hedging potential of non-conventional assets like cryptocurrency (Bitcoin), FinTech equities (FINXs), and green bonds (QGREENs) against traditional equity benchmarks, namely the MSCI World and MSCI Emerging Markets indices using daily data from 2016 to 2021. Employing Time-Varying Parameter Vector Autoregression (TVP-VAR), network connectedness analysis, and the Minimum Connectedness Portfolio (MCoP) approach, the study uncovers dynamic interdependencies among these markets. The results reveal that Bitcoin consistently acts as a net receiver of shocks, providing strong diversification benefits during crisis periods, such as the COVID-19 pandemic. FinTech assets show moderate resilience, while green bonds primarily serve as shock transmitters with limited hedging ability. Optimal portfolio weights indicate the highest allocation to Bitcoin, followed by FinTech and green assets, supporting their inclusion in diversified portfolios. Overall, the findings underscore Bitcoin’s superior risk-mitigating role and highlight the strategic importance of digital assets in achieving portfolio stability and sustainability in volatile global markets.

No abstract available

Traditional valuation methodologies demonstrate significant limitations when applied to green and impact investments, as they fail to capture the complex interplay between financial performance and environmental, social and governance characteristics that drive sustainable investment values. This study empirically evaluates machine learning approaches to valuation of green and impact investment using comprehensive data from the United States sustainability equity and bond market spanning 2010-2024. The research constructs multiple machine learning models that incorporate financial variables, ESG performance metrics, carbon footprint data, green bond certification information and macroeconomic factors to predict equity returns, bond yields and valuation multiples. A systematic literature review methodology was used to examine the theoretical foundations, traditional valuation limitations, machine learning applications in finance, ESG data infrastructure and empirical evidence on sustainability performance relationships. The findings revealed that machine learning techniques, particularly reinforcement learning algorithms, gradient boosting decision tree and LSTM-based models, achieve prediction accuracy exceeding 90% and demonstrate superior capability to process heterogeneous ESG data compared to conventional approaches. However, significant challenges exist regarding ESG measurement standardization, with correlations between major rating providers ranging from 0.38 to 0.71. The study, therefore, concludes that machine learning offers transformative potential for sustainable investment valuation however, it highlights relevant data quality issues that require resolution for optimal model performance. Keywords: Machine Learning Approaches, Valuation of Green, Impact Investments, U.S. Sustainable Equity, Bond Markets.

The present study aimed to evaluate the return performance and variability of green finance investment stocks and conventional stocks over the past five financial years. The research sought to assess whether green finance stocks’ investment offers competitive and stable returns compared to traditional equity investments. By means of data from the last five financial years, this study computed single-period Ex-post return, Arithmetic mean return, Geometric mean return, and multiple-period holding period of returns for both Green and Conventional stock groups. Despite the use of inferential statistical techniques, including single-factor ANOVA, F-test, and Student T-test, to test the significance of differences in mean return and variances. The single-factor ANOVA-shaped F Calculated value, which was below the critical value with a P value of 0.0746 (approx.), indicates that the mean return in between the two groups was not statistically different. However, the F-test showed a one-tailed, F-calculated value and P value 0.0012 (approx), suggesting a significant difference in return variances. The Student T test was also applied to confirm that the returns were statistically comparable. The results provide an actionable insight for Investors considering Green Finance Instruments. The study enhanced limited empirical research on green finance by providing a five-year comparative analysis of green vs conventional stock returns comparable to risk using statistical methods.

When and how does sustainable finance tangibly contribute to creating a better world? In this paper, we outline mechanisms through which impact on sustainability outcomes is transmitted from financial systems to the real economy. We argue that, to have a positive impact on sustainability outcomes, financial institutions must make a clear contribution to: (1) reducing (increasing) the cost of capital for firms’ (un)sustainable activities; (2) increasing (reducing) their access to liquidity; and (3) encouraging or enabling sustainable corporate practices. We assess the potential for impact in each category across several asset classes. We analyse how financial institutions can integrate the development of “impact budgets” into strategic asset allocation. Finally, we consider ways in which future research could consider the implications for impact-oriented portfolio construction in more detail and develop empirical methods for further testing and quantifying the impact of the different transmission mechanisms we discuss.

The rapid expansion of sustainable investment markets has intensified the need for accurate Environmental, Social, and Governance (ESG) data analysis to support effective capital allocation. However, traditional analytical approaches often struggle to interpret the growing volume of heterogeneous sustainability information, leading to information asymmetry and suboptimal investment outcomes. This study examined the role of artificial intelligence-driven ESG signal processing in enhancing green finance intelligence and improving sustainable capital allocation and capital market efficiency. A quantitative research design was employed, utilizing primary data collected from 312 financial professionals working in banking, asset management, and financial technology sectors. Descriptive statistical analysis revealed strong agreement regarding the importance of AI adoption in sustainable finance, with artificial intelligence adoption recording the highest mean value (M = 4.14, SD = 0.69), followed by green finance intelligence (M = 4.09, SD = 0.68) and ESG signal processing capability (M = 4.07, SD = 0.72). Regression analysis demonstrated that artificial intelligence adoption significantly influenced green finance intelligence (β = 0.41, t = 7.86, p < 0.001), while ESG signal processing capability significantly improved sustainable capital allocation (β = 0.38, t = 6.94, p < 0.001). Additionally, green finance intelligence positively affected capital market efficiency (β = 0.35, t = 6.21, p < 0.001). The findings indicated that artificial intelligence significantly enhances ESG information processing, reduces information asymmetry, and supports data-driven sustainable investment strategies. 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The number of equity funds claiming to be sustainable continues to grow, as well as the regulatory transparency requirements applicable to these funds. Despite this upward trend, sustainable finance is facing a double identity crisis: On the one hand, civil society is increasingly denouncing financial greenwashing and questioning the real impact of sustainable finance, and on the other hand, practitioners are divided on the (double) materiality definition and the most effective levers to have an extrafinancial impact. This article aims to assist institutional asset owners faced with these questions as they try to build a sustainable portfolio, whether indexed or active. Based on a review of the academic literature, we highlight that the main building blocks that investors ought to consider—themes, levers (exclusions, allocation, engagement), and data—are interdependent. We then propose a classification of sustainable investments, as well as different levels of ambition in terms of extrafinancial impact that lead to four families of coherent sustainable investment strategies that combine themes, levers, and data in a consistent way.

This study examines the impact of Government Sustainable Investment (GSI) on corporate financial performance. Using the Environmental Ratio (ER) as a proxy to measure the intensity of regional policies, we perform linear regression analysis on financial data of 15 firms spanning three sectors: technology, finance, and automotive. Key findings reveal: the finance sector demonstrates a positive effect on efficiency first, though revenue and profit trends remain unstable; the technology sector exhibits overall neutrality in the short term, largely influenced by its globalized revenue structure and the time lag between R&D and commercialization; Automotive manufacturing, which is undergoing compliance-related investments and production line transformation, shows no significant improvement. Overall, GSI influences finance through structural channels such as governance, asset allocation, and operational efficiency, exhibiting distinct time lags and industry-specific heterogeneity. We recommend shifting evaluation metrics from short-window profits to efficiency and risk exposure indicators, while extending observation periods. Limitations include sample size, proxy variables, and window length; future research should expand multinational samples and incorporate sustainability performance metrics such as carbon efficiency indices.

This paper investigates the transformative potential of financial institutions in driving the shift toward a regenerative economy, with a particular emphasis on embedding circularity within financial and economic systems. In the face of escalating ecological degradation, resource exhaustion, and systemic inequality, there is an urgent imperative to reimagine traditional economic models through restorative and sustainable frameworks. This study critically examines the role of banks, investment firms, and allied financial entities in operationalizing circular economy (CE) principles across lending practices, asset allocation, and investment portfolios. It explores key mechanisms such as green bonds, circular economy-linked loans, sustainable finance instruments, and the financing of closed-loop supply chains, assessing their capacity to enable regenerative business transitions. The paper also interrogates the influence of evolving policy regimes and regulatory frameworks in either enabling or constraining financial sector alignment with circular imperatives. Drawing from interdisciplinary literature spanning sustainable finance, ecological economics, and institutional theory, the research identifies both structural barriers and emergent opportunities shaping the financial sector's response to circularity. Findings reveal that while promising innovations exist, institutional inertia, risk perception biases, and valuation misalignments remain critical obstacles. Nevertheless, the study contends that financial institutions hold a catalytic role in accelerating systemic circular transitions if supported by coherent policy instruments, reconfigured risk models, and metrics that reflect long-term ecological value. The paper concludes that advancing a regenerative economy requires integrated approaches that converge finance, governance, and sustainability science to embed circularity at the core of capital flows and economic design.

Abstract The transition towards renewable energy is crucial in addressing climate change and ensuring sustainable economic development. However, investment decisions in the renewable energy sector are influenced by various factors beyond traditional financial metrics. This study explores the role of investor sentiment in driving investment flows into renewable energy assets. Drawing upon the principles of behavioural finance, the research examines how psychological factors, market moods, and sentiment indices impact investment decisions, asset pricing, and the valuation of renewable energy companies and projects. Using both qualitative and quantitative methods, including sentiment analysis of market data and investor surveys, the thesis investigates the correlation between investor sentiment and investment patterns in renewable energy markets. The findings reveal that positive investor sentiment significantly enhances capital allocation towards renewable energy initiatives, while periods of negative sentiment increase volatility and risk perception. The study provides valuable insights for policymakers, investors, and stakeholders seeking to promote sustainable investments by understanding and leveraging behavioural factors. Ultimately, this research contributes to the growing literature on sustainable finance and behavioural influences in green investments.

The FinTech revolution is changing the way banks work around the world by combining blockchain and artificial intelligence (AI) to make safe, efficient, and customer-focused financial environments. A systematic review of AI blockchain convergence in modern banking, emphasizing its transformative impact on security, operational efficiency, and financial innovation. AI enables intelligent decision-making through applications such as fraud detection, credit risk assessment, algorithmic trading, and predictive analytics, while blockchain provides decentralized, tamper-resistant, and auditable transaction infrastructure. Digital currencies, asset tokenization, decentralized finance (DeFi), smart contracts, and automated regulatory compliance are some of the new FinTech applications driven by their synergy. This integration also supports Environmental, Social, and Governance (ESG) by facilitating real-time fund allocation, sustainable investment tracking, and transparent auditing. Despite its significant potential persisting, including regulatory ambiguity, scalability limitations, cybersecurity risks, and data privacy concerns, which limit large-scale adoption in banking systems. By synthesizing and analyzing key technological trends, the current capabilities of AI–blockchain integration in FinTech that the synergistic convergence of AI, blockchain, and financial technologies is a critical enabler for next-generation digital banking, promoting financial inclusion, resilience, and sustainable economic growth

ABSTRACT A growing number of investors are adopting net-zero targets. Based on semi-structured interviews with 20 asset managers – primarily investing in public equities and fixed-income – this paper investigates the factors influencing target-setting. Novel to the literature, we show that investor coalitions have played a central role in the institutionalisation of net zero, including through the dissemination of ‘best practice' guidance. However, significant variations are found in the degree to which asset managers have aligned with, or even exceeded, this guidance. To understand this heterogeneity, we propose a new typology, which distinguishes investors as hedgers, fast followers, and leaders. A combination of internal factors (such as resources and organisational values) and external pressures (including client preferences and regulatory contexts) are shown to explain these variations. Our analysis reveals that net-zero target-setting is largely a continuation of asset managers’ past responsible investment practices, shaped by their existing capabilities, beliefs, and client bases.

Since the 2015 Paris COP21 meeting, the financial services industry has increasingly been expected to play a pivotal role in driving the sustainable economic transition. Our review examines and compares the various strategies employed by key segments of this industry—namely institutional investors, asset managers, venture capital firms, insurers, and bond issuers—to foster a more sustainable economy. While our literature review reveals significant industry responses, it also highlights that the voluntary nature of many initiatives and the lack of standardized regulatory frameworks pose considerable challenges to transforming business models in line with sustainability goals. Achieving sustainable finance will ultimately require coordinated and concerted efforts among governments, financial institutions, and nongovernmental organizations.

This article examines the role of the Big Three asset management firms – BlackRock, Vanguard and State Street – in corporate environmental governance. Specifically, it investigates the Big Three’s relationships with the publicly listed Carbon Majors: a small group of fossil fuels, cement and mining companies responsible for the bulk of industrial greenhouse gas emissions. Engaging with the corporate governance concepts of ownership and control, and exit and voice, it charts the rise to prominence of the Big Three, including their environmental, social and governance (ESG) funds, in the ownership of the Carbon Majors. Having established their status as key sources of permanent capital that are unlikely to exit from their investment positions in the world’s most polluting publicly listed corporations, the article examines how control may be exercised through voice by analysing the Big Three’s proxy voting record at Carbon Major annual general meetings. It finds that they more frequently oppose rather than support shareholder resolutions aimed at improving environmental governance and that their voting is more likely to lead to the failure than to the success of these resolutions. Remarkably, there is little to distinguish the proxy voting of the Big Three’s ESG funds from their non-ESG funds. Regardless of whether these resolutions succeeded or failed, they also tend to be narrow in scope and piecemeal in nature. Overall, the article raises serious doubts about the Big Three’s credentials as environmental stewards and argues instead that they are little more than stewards of the status quo of shareholder value maximization.

This study employs the gradual implementation of China’s Environmental Credit Evaluation Policy (ECEP) as an exogenous shock to analyze the heterogeneous effects of credit-based environmental regulation on financial asset allocation across enterprises with distinct ownership structures – specifically, between non-state-owned enterprises (non-SOEs) and state-owned enterprises (SOEs). Using data from A-share listed companies in Shanghai and Shenzhen spanning 2008–2022, this study employs a staggered difference-in-differences (DID) design to identify causal effects. ECEP exerts divergent effects on corporate financial asset allocation across ownership structures: it increases allocation in non-SOEs while decreasing it in SOEs. This divergence is attributed to ECEP’s impact on corporate motivations: it strengthens risk-aversion and profit-seeking motives in non-SOEs, driving their increased financial asset allocation, but weakens these same motivations in SOEs, leading to reduced allocation. Confirming this behavioral pattern, ECEP reduces green investment in non-SOEs but increases it in SOEs, corresponding to an inverse effect on their financial asset allocation. Furthermore, cross-sectional analysis reveals that heightened local government environmental attention, increased local government environmental subsidies, stronger local government environmental penalties and advanced regional marketization collectively mitigate ECEP’s promoting effect on non-SOEs and amplify its inhibitory effect on SOEs. This study offers important implications for corporate managers. Strategic recalibration is essential. Non-SOE executives must reconcile short-term gains with long-term sustainability: although ECEP-driven financial asset allocation boosts immediate returns, reduced green investment jeopardizes environmental credibility and future funding access. Managers should allocate a fixed percentage of financial returns to green innovation while proactively disclosing environmental strategies to maintain stakeholder confidence. Conversely, SOE leaders should institutionalize green transition gains by embedding environmental KPIs into executive compensation to align with regulatory incentives and leveraging green asset securitization to offset liquidity pressures from shrinking financial investments, thereby funding sustainable expansion amid intensified policy synergies. This study yields critical implications for policymakers. First, ownership-specific regulatory mechanisms should be designed to address the diametrically opposite effects of ECEP: whereas non-SOEs increase financial asset allocation, SOEs reduce it. For non-SOEs, mandatory green investment thresholds should counterbalance their tendency to reduce environmental expenditures. For SOEs, incentives such as green credit subsidies or tax relief would consolidate proactive green transitions. Second, policymakers should construct synergistic policy bundles, given that heightened local government environmental attention, increased local government environmental subsidies, stronger local government environmental penalties and advanced regional marketization collectively attenuate ECEP’s positive effect on non-SOEs while amplifying its inhibitory effect on SOEs. This requires integrating ECEP with fiscal tools (e.g. tiered subsidies/penalties tied to credit tiers) and establishing market-based linkages (e.g. interest rate discounts for high-credit enterprises in developed markets) to incentivize sustainable capital allocation. This study demonstrates an ownership-differentiated effect of credit-based environmental regulation on corporate financial asset allocation. Grounded in institutional theory, it advances a theoretical framework elucidating corporate motivations and behaviors concerning financial asset allocation under environmental credit pressure. Consequently, the findings yield significant implications: policymakers should develop ownership-specific regulatory mechanisms and implement synergistic policy bundles, while corporate managers must undertake strategic recalibration to foster sustainable development.

The current research compares the performance of traditional and sustainable finance companies in India. Since sustainability principles are gradually gaining prominence in the context of economic growth and development, especially in new economies such as India, this research proposes to interrogate the possibility of a firm that provides sustainable finance independently generating the financial competitiveness of conventional firms. The research adopts a quantitative comparative research design to compare the financial ratios, risks, and performances of traditional and sustainable automotive firms listed in global stock markets for the last five years. The study shows that those firms operating in the sustainable finance sector offer higher growth rates, better profitability, and optimal asset turnover compared to the other companies. They also use more responsible behaviors concerning debts. Some banks from the traditional finance sector, especially those in the developed nations, have high profitability and efficiency but tend to exhibit more fluctuations in their profitability and more dependency on credit in periods of economic turmoil. These findings have major consequences for investors, policymakers, and heads of the organization. To individual investors, sustainable companies are likely to provide better, longer-term potential investment opportunities. Stakeholders in these sectors should consider these findings to foster fresh development in the insurance industry and to enhance stability in both industries. The present research shows that sustainable practice can be incorporated into a financial plan to improve the profitability, and the company's longevity and thus make the Indian financial industry stronger and more sustainable.

This research investigates the epistemological reconfiguration of Environmental, Social, and Governance (ESG) integration within Vietnam's nascent sustainable finance ecosystem, examining investor decision-making paradigms through a multi-theoretical analytical framework. Employing a quantitative methodological approach utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) with complementary fuzzy-set Qualitative Comparative Analysis (fsQCA), this study analyzed data collected from 287 institutional investors operating within Vietnam's financial markets. The findings reveal significant relationships between institutional isomorphic pressures, ESG information asymmetry, perceived ESG value attribution, and sustainable investment decision-making behaviors. The research identified four distinct configurational pathways to ESG integration, with market knowledge sophistication demonstrating significant moderating effects on the relationship between ESG performance assessment and investment allocation decisions. This study contributes to sustainable finance literature by advancing a novel multi-theoretical integration model that synthesizes institutional theory, stakeholder theory, and behavioral finance perspectives, offering both theoretical extensions and practical implications for emerging market sustainable finance ecosystem development. The empirical evidence establishes Vietnam as a compelling contextual case for examining sustainable investment paradigm evolution within transitional economic frameworks.

Climate change and broader environmental risks have emerged as critical challenges for global financial systems. These risks, transmitted through physical damage, transition policies, and changing market dynamics, can undermine financial stability, disrupt credit provision, and affect asset valuations. Central banks, whose primary mandates are price stability and systemic robustness, are increasingly called upon to address these challenges by integrating sustainability into their frameworks. This paper explores the evolving role of central banks in advancing sustainable finance. It examines how climate-related and environmental risks affect monetary policy transmission, financial sector soundness, and investment strategies. The analysis focuses on three main areas where central banks are responding: (i) monetary policy operations, including collateral frameworks and asset purchases; (ii) financial stability and supervision, with an emphasis on climate risk stress testing and disclosure requirements; and (iii) portfolio management and central bank operations, where many institutions are taking steps to align their investments with sustainability objectives and reduce their own carbon footprints. The study adopts a comparative approach, discussing sustainable finance practices of central banks across Europe, Asia, and other regions. It highlights the diversity of approaches shaped by institutional mandates, regulatory environments, and levels of exposure to climate risk. By examining both advanced and emerging economies, the paper identifies common trends, such as the increasing use of scenario analysis, the integration of environmental, social and governance (ESG) considerations into supervision, and the development of taxonomies and disclosure frameworks to mitigate greenwashing. The findings suggest that while there is no one-size-fits-all model, central banks globally are moving toward greater recognition of climate and environmental risks as material financial risks. Moreover, central banks are increasingly seen not only as regulators and supervisors, but also as market participants and leaders by example. This evolving role underscores both the opportunities and the challenges of embedding sustainable finance within the mandates of central banking, particularly in balancing traditional policy goals with long-term sustainability objectives.

Commonwealth Small Island Developing States (SIDS) are broadly recognised as facing considerable challenges in accessing finance for development and climate change adaptation. Conventional approaches to overcome these challenges emphasise investing in capacity building to improve the quality of SIDS domestic governance as the key factor in facilitating more investment. We argue that this strategy has had limited success because it has failed to derisk SIDS specific systemic barriers faced by SIDS governments and investors. To address this, we introduce the Common Pool Asset Structuring Strategy (COMPASS) as a new type of financial governance framework and pathway for SIDS to access international finance and investment. Using the principle of collaboration, this model would shift SIDS from making individual applications for finance, to collectively developing investable projects, at larger scales, that can exploit common investment opportunities - thereby increasing scale, driving down project costs and diversifying project risk profiles.

This research investigates the effectiveness of green bond issuance by Bank Mandiri in strengthening the consistency and implementation of sustainable finance. The research results show that the issuance of green bonds has a significant positive impact on the application of sustainable finance principles by Bank Mandiri. The presented data indicates that the amount of funds raised and the level of investor participation are very high, reflecting interest and commitment to sustainable projects. Green bonds also demonstrate superiority over other sustainable financial instruments, such as carbon taxes and green sukuk, with broader allocation flexibility and efficiency in fund raising. The issuance of green bonds also has a positive impact on Bank Mandiri's financial position, with changes in capital structure, cash flow, and investment strategies that support sustainability. In conclusion, the issuance of green bonds by Bank Mandiri successfully reinforces the commitment to sustainable finance, attracts investor interest, and supports projects that have a positive impact on the environment and society. Bank Mandiri has demonstrated that through green bonds, the bank can play an important role in creating a more sustainable and inclusive economy. 

Exclusion/negative screening is the most popular methodology used to integrate environmental, social, and governance (ESG) criteria into investment strategies. It consists of excluding from the investment universe the instruments issued by companies that don’t meet the criteria defined in the manager’s investment policy. This method is often applied in the passive investment space, in which exclusion criteria are combined with index replication. In this article, the authors perform an extensive study of the impact of exclusion policies on the financial risks of 493 indexes from Developed Europe and the US. To address the lack of consensus on ESG criteria, the authors built three screens based on typical investment policies: a screen based on a few consensual criteria, a more comprehensive screen that incorporates additional climate net-zero criteria, and finally an ambitious screen eliminating all companies that have a negative contribution to any of the United Nations’ sustainable development goals. The authors show that the effects of the first two screens on index risks are often very limited, especially when using an optimized reallocation method.

This study investigates the excess value implications of news about biodiversity risk for investors of diversified firms using a sample of 1019 US firms from 2001 to 2023. In a given year, more positive news about biodiversity risk increases the value of diversified firms relative to a benchmark portfolio of single‐segment firms, especially for large‐diversified firms. This diversification premium effect, that is, the excess value of diversified firms, in response to positive news about biodiversity risk, is non‐linear, robust to several alternative specifications, and exists regardless of internal capital market efficiency, number of business segments, excess net income, and the climate change exposure of diversified firms. Our study highlights the potent role of diversified firms in exploiting biodiversity protection‐related investment opportunities, as investors attach a relative premium to such firms.

What shapes fossil-fuel investment and divestment decisions? What are pension funds’ climate-related considerations? And how do conceptions of portfolio risk influence these issues? Danish pension funds constitute a rare and understudied cohort of investors who have undertaken comparatively progressive fossil-fuel investment decisions. Simultaneously, diversification and market rationality have frequently been invoked as obstacles to divestment and active ownership. Using the Danish experience, this article conducts an archaeological analysis of the concept of portfolio risk, unearthing the various ways in which it has shaped fossil-fuel investment decisions. The analysis identifies five key aspects through which the concept has hampered Danish pension funds’ active ownership and fossil-fuel divestment decisions (sector diversification, externalities, market rationality, dispersed ownership, and passive index investing). The article argues that these discursive aspects have reinforced a passive tendency within finance capitalism to bolster the status quo, thereby supporting prevailing market actors and the continued extraction of fossil fuels.

PurposeThe survivorship of firms under extreme weather poses an essential question about the local economy's health. Over 90% of agricultural banks are categorized as community banks, which are important financial institutions promoting local growth. While previous studies suggest that climate change and weather shocks adversely impact community banks' resiliency, studies on whether these institutions engage in risk-reducing management strategies have been limited. In this study, the authors examine strategic choices of local community banks when facing flood events which include (1) safety net increase, (2) portfolio diversification, and (3) branch opening. These strategic choices are the coping mechanisms banks can take to survive while affecting the local competitive lending market.Design/methodology/approachThe authors use panel-fixed effect regressions based on the storm data from National Oceanic and Atmospheric Administration (NOAA)'s National Weather Service (NWS) and the call reports from the Federal Deposit Insurance Corporation (FDIC). The authors focus on community banks' account variable characteristics and the number of offices to examine whether community banks take an active role in managing flood risk.FindingsResults suggest that community banks do employ the selected strategic choices to a certain degree, as it is found that there is an increase in the core capital that absorbs shocks and portfolio diversification. However, the magnitudes of these activities are rather small and not large enough to fully mitigate the climate risk. Also, the authors do not find any evidence of branch expansion associated with local floods.Originality/valueThis study contributes to the literature by examining different strategic choices of community banks in the face of natural uncertainty. Even though concerns of climate risk have been raised in the regulatory setting, a lack of guidance or assessment tools could contribute to the passive action of these community banks, even though climate risks can have a significant economic impact. Thus, the evidence documented from this study calls for further guidelines and the importance of highlighting climate risks on community banks so that they can actively engage in risk-reducing strategies.

Climate change poses new challenges for portfolio management. In our not-yet-low carbon world, investors face a trade-off between minimizing their exposure to climate risks and maximizing the benefits of portfolio diversification. This paper investigates how investors and financial intermediaries navigate this trade-off. After the release of Morningstar’s novel carbon risk metrics in April 2018, mutual funds labeled as “low carbon” experienced a significant increase in investor demand, especially those with high risk-adjusted returns. Fund managers actively reduced their exposure to firms with high carbon risk scores, especially stocks with returns that correlated more with the funds’ portfolios and were thus less useful for diversification. These findings shed light on whether and how climate-related information can re-orient capital flows in a low carbon direction.

, including energy transition risk and physical impact risks to its business, using several in-depth analytical approaches that tested the sensitivity of AES’ gross margin across the entire business. The report discusses the impacts of its findings on AES’ strategy both in terms of business risks and opportunities, as well as how the company is managing physical risk by building its assets for future climate risk exposure and diversification of its portfolio, sourcing power across 15 countries and shifting to smaller, renewable assets. AES developed the process and results of the scenario analysis by forming a steering committee of representatives across the company including from financial planning, corporate risk and strategy, legal, operations and other teams.

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This study examines the strategic role of green finance in improving the financial performance of Chinese commercial banks after 2020. Using a cross-sectional dataset of 128 banks that were actively engaged in green lending, green bond issuance, and environmental innovation between 2020 and 2025, we employed multivariate regression models that controlled for size, leverage, and portfolio-related CO 2 emissions. The results show that green credit allocation and environmental innovation have a strong, positive, and statistically significant effect on return on equity (ROE), while higher environmental, social, and governance (ESG) disclosure scores consistently improve profitability by reducing perceived risk and attracting responsible investment. In contrast, corporate social responsibility (CSR) initiatives display a negative short-term effect, reflecting their delayed financial payoffs. Surprisingly, ESG controversies demonstrate a positive association with ROE, highlighting the potential value of proactive crisis management, and market expectations. By integrating environmental innovation and ESG controversies into the green finance–performance nexus, this research advances existing literature and provides policymakers and banking executives with actionable insights. Our findings emphasize that green finance is a strategic tool that can be used to reconcile profitability with sustainability, thereby reinforcing the alignment between financial and climate objectives in emerging economies.

In the context of sustainable development, optimizing green project priority decisions through project portfolio management has become a key issue for enterprises. Based on the theory of planned behavior and social norms, this study constructed a theoretical model that combines psychological factors and social factors. It aims to explore the joint effect mechanism of environmental attitudes and social norms on green project priority decisions. The study used a questionnaire survey method to collect data from 294 business managers from industries such as manufacturing, information technology, and finance. The scales used included the New Ecological Paradigm Scale, Social Norm Scale, and Project Priority Evaluation Scale. Data analysis was conducted using SPSS 25.0 and the PROCESS plugin to examine the main and moderating effects. The results showed that environmental attitudes had a significant positive impact on the priority of green projects (β=0.39, p<0.001), and social norms played a positive moderating role in the relationship between the two (β=0.18, p<0.01). Research has shown that green project decision-making is not only driven by individual attitudes but also strengthened by external regulatory environments. However, the research samples mainly come from specific industries and regions, and the universality of the conclusions needs further verification. Practically, enterprises should focus on cultivating managers' environmental values and actively shaping and utilizing social norms to promote higher priority for green projects in resource allocation.

Objective: This study aims to map and analyse the relationship between green bonds, sustainable finance, and other financial assets to understand thematic developments, empirical findings, and research gaps in the post-Paris Agreement era. Method: A systematic literature review was conducted using the SPAR-4-SLR protocol combined with bibliometric analysis through VOSviewer and Biblioshiny on 110 peer-reviewed articles published between 2018 and 2025. Results: The analysis reveals an exponential growth in publications with an annual rate of 55.12%, highlighting the rapid expansion of the sustainable finance research ecosystem. Empirical evidence consistently confirms the presence of a greenium—green bonds offering lower yields than conventional bonds—along with diversification benefits and reductions in portfolio volatility. Thematic mapping identifies six dominant clusters: sustainable finance, green finance, ESG/SDGs, renewable energy, portfolio and risk, and green innovation, while investor attention emerges as an under-studied moderating variable in cross-asset dynamics. Novelty: This study contributes by identifying the most influential works, structuring the intellectual landscape of sustainable finance, and proposing a future research agenda focused on deepening cross-asset connectivity and enhancing policy relevance, particularly in emerging markets such as Indonesia.

Abstract The European Union (EU) Green Bond Regulation aims to further align private investments with the Paris Agreement, minimise greenwashing and enhance transparency, offering a robust framework for European Green Bonds. While the academic research actively explores green bonds as part of the investment portfolio or environmental, social and governance agenda, relatively less interest is dedicated to the regulatory challenges. The aim of this paper is to analyse the impact of the EU Green Bond Regulation on the Baltic green bond market, focusing on its implications for market development, regulatory harmonisation and sustainable finance practices. The study combines a comprehensive literature review with an analysis of the EU Green Bond Regulation, the EU Taxonomy Regulation and related frameworks such as the Sustainable Finance Disclosure Regulation and the Climate Bonds Standard. The findings reveal that Baltic green bond market remains dominated by the state-owned enterprises that currently comply with international standards and EU regulations. The EU Green Bond Regulation, while stimulating market development, introduces overlapping requirements that may create administrative burdens and additional costs for issuers, especially the private segment.

This paper studies the green new product development (GNPD) problem of a risk-averse capital constrained supply chain (SC). The SC is managed by an SME entrepreneur, seeking financial support from a multi-sided FinTech platform (MSP) to develop a portfolio of green and non-green products. The MSP offers the SC a combination of equity financing (EF) and debt financing (DF) facilities and must decide on the interest rate of its DF facility. Using a benchmark model, we first characterize the SC’s production and the MSP’s financing decisions under a deregulated scenario. Focusing on an alternative case with government intervention (i.e., hybrid environmental-green entrepreneurship policy), we next develop a three-level game theoretical model and sequentially characterize the decision-making behavior of government, MSP, and SC. The model outcomes are analyzed by considering the policy approach (i.e., economic influence vs. social welfare) and the platform’s risk attitude. The results reveal that, when coupled with an appropriate government intervention policy, a regulated scenario leads to a better outcome, particularly when the MSP is risk-neutral and strikes a right balance between the EF and DF. The win–win situation may not be realized when the MSP is risk-averse and the host government is merely focused on its economic influence. To successfully promote sustainable supply chain finance (SSCF) through digital platforms, policy makers are urged to leverage their legislative power and prioritize green entrepreneurship and social welfare over their financial maximization agenda.

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In today's economic world, due to the growth of the capital market, the importance for people to invest has increased. The most important concern for investors is choosing the best portfolio for investment. For complex decisions in which the decision maker is ambiguous, such as portfolio selection, using the multi-criteria decision making (MCDM) technique to prioritize options and decide on the optimal choice is the best solution. In this research, a generalization of this method utilizing the intuitionistic fuzzy analytic hierarchy process (IFAHP) was discussed. Considering the importance of this topic in today's economy, the purpose of this research was to describe and apply the new integrated technique of IFAHP for ranking the portfolio of companies admitted to the Tehran Stock Exchange. For this purpose, using the statistics published by the Tehran Stock Exchange, six companies including Jabra Ben Hayyan, Kaghazsazi Kaveh, Laabiran, Daro Luqman, Pashme Shishe Iran, and Bourse Kala Iran were examined. These companies were the best options for investment according to the charts and indices of the stock exchange at the time of our research. Finally, using the technique mentioned above, we described the evaluation and ranking of portfolios for confident and efficient decision -making.

The green bond market and related scholarly research have grown exponentially recently. However, the literature remains scattered. This study addresses the gap by analyzing 744 Scopus‐indexed journal articles using a mix of bibliometric and content analyses to unveil trends, current dynamics, and knowledge structures. The bibliometric analysis shows dynamic transformation and differential development among clusters. It indicates the following three prominent research themes: (a) reducing carbon emissions, (b) premium and financing, and (c) connectedness and portfolio risk effects. From policymakers' perspective, the content analysis reveals positive linkages and channels between green bonds and the environment. Pertinent to issuers and investors, estimates of green bond premiums vary by time, context, and methodology. Future scholarly research should focus on causality, regional variations, and the role of policy design in the impact of green finance. Researchers must further investigate the motives of green bond investors and issuers.

Green bonds (or climate bonds) are one of the most used sustainable investment instruments, and under the Paris Climate Agreement of 2015, the climate bond market is expected to thrive soon. Green bonds are gaining increasing popularity among environmentally responsible investors, as well as investors who “simply” attempt to benefit from portfolio diversification, including green issuances, that are close to other fixed bonds. This paper aims to take advantage of previous literature contributions on the green bond market to indicate the way forward for future research. Herein, through a systematic literature review on the green bond market, our goal is to provide investors, main markets actors, and policymakers with some helpful insight on the role of environmental investments in reshaping the financial markets and fostering the sustainability of the economy. Key words: Green bonds; systematic literature review; climate bond introduction, sustainable investment

The purpose of this study is to provide a literature review of green bonds and their relation with other financial assets. Most of the research that has been conducted has focused on the spillover transmission from the financial asset market to the green bond market. The method used to select and analyze the results of journal reviews is Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The journals used in this study are Scopus-indexed journals, which are searched using the keywords green bond, cointegration, transmission, and spillover. The result indicates that green bonds can be used as an alternative in diversifying portfolio instruments. Based on previous studies, it was found that there was spillover transmission from the financial asset market to the green bond market. This indicates that volatility in the financial market will spill over and affect the green bond market. This study can be used as a strategy for making investment decisions, especially in building investment portfolios.

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After the formation of the Chinese green bond market in 2016, it rapidly developed. However, there have been relatively few studies in academia on the financial performance of enterprises after issuing green bonds. In order to promote the development of the green bond market and guide enterprises to improve their investment and financing decisions, this paper uses panel data of A-share listed companies and green bond issuing companies from 2012 to 2022 as samples to study the impact and mechanism of green bond issuance on the investment and financing behavior and maturity mismatch of enterprises. The conclusion drawn is that green bond issuance significantly promotes the improvement of enterprises' investment and financing capabilities, and it also reduces the degree of maturity mismatch for enterprises. This conclusion passes the parallel trends test and remains robust after controlling for the effects of corporate financial asset allocation, industry time trends, and excluding competitive hypotheses. Through mechanism research, it is found that green bonds can improve the level of enterprise investment and financing and the degree of maturity mismatch by reducing the degree of financing constraints on enterprises. They can also enhance enterprise investment and financing capabilities by changing market sentiment and alleviate the maturity mismatch of investment and financing.

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In recent years, an increasing number of companies have embraced Environmental, Social, and Governance (ESG) principles as a driving force for sustainable development. ESG investment concepts, with green finance as a representative, have gained widespread acceptance. This paper utilizes the event study method, using SF Holdings as a case study, to analyze the issuance costs and economic effects of its green bonds. This analysis aims to provide insights for similar enterprises engaging in green finance and further enrich the theoretical research related to green finance.

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In forming their portfolios, investors should analyze the risk and return of each investment instrument. This is aimed at preventing investors from speculating and gambling with their investments. Conducting an investment portfolio optimization study on LQ-45 stock index, government bond, USD, gold, and Bitcoin can provide valuable insights due to unique market characteristics in Indonesia. This research analyzes the formation of investment instruments over the last 60 months, specifically from January 2018 to December 2022. The research method used in this study is quantitative research aimed at selecting several investment instruments for a portfolio in Indonesia. The portfolio aims to minimize risk and maximize return using the Markowitz method, also known as the optimal portfolio. To fulfill the objectives of this research, data on the prices of each instrument are required. An optimal portfolio can be obtained by combining two instruments: 18% bitcoin and 82% gold. This optimal portfolio can achieve an expected return of 1.29% with a risk level of 5.15%. Considering a risk-free rate of 0.375%, this portfolio forms a slope of 0.1775, which is the largest slope formed between the combination of risk-free instruments and risky portfolios. Investors should allocate their funds more wisely, considering not only the highest return but also the associated risk. High returns often come with high risks, so investors need to assess the risk-return trade-off before making investment decisions.

 The impact of urban investment bonds on green financial innovation have been controversial. As some assume that it leads to a negative impact while some stands in the opposition. This paper reveals evidences of the effects of urban investment bonds on green financial innovation by literature review, case study, statistical analysis, and comparative study methods. The study result shows that the introduction of China’s urban investment bond with significant financing scale have filled the large funding gap in climate investment and provides efficient support for dealing with climate change issues. Saudi Arabia’s case have demonstrated its success in using urban investment bonds on the construction of NEOM New City which aims to build a smart city powered entirely by clean energy. It effectively improves sustainability and copes with environmental issues. With the findings, government and policy-makers of countries around the world should take into account with greater scale of promoting urban investment bonds to green financial innovation, which could help the economy more sustainable and cope with environmental issues around the world.

In an era of growing financial awareness, particularly among younger generations, passive investing has gained significant popularity. This paper examines the creation of investment portfolios tailored to different risk appetites—conservative, moderate, and aggressive. The study analyzes the risk-return profile over ten years using a sample that includes various asset classes such as gold, real estate, equity exchange-traded funds (ETFs), bond ETFs, and Bitcoin (BTC). The findings highlight the superior returns of cryptocurrency and equity-based aggressive portfolios, contrasting these with the stability and lower yields of more conservative investments. The paper identifies a statistically significant correlation between asset classes and proposes strategies for constructing portfolios that balance risk and reward. Additionally, the research explores generational shifts in investment behaviors, emphasizing Generation Z's growing preference for high-risk assets like cryptocurrencies. This paper contributes to the understanding of passive portfolio management and offers insights for investors seeking to optimize returns based on their risk tolerance.

This study examines the effect of the investment portfolio structure on insurers’ solvency, as measured by the Solvency Capital Requirement ratio. An empirical sample of 88 EU-based insurance groups was analyzed to provide robust evidence of the portfolio’s impact on the Solvency Capital Requirement ratio from 2016 to 2022. Linear regression and supervised machine learning models, particularly extra trees regression, were used to predict the solvency ratios, with the latter outperforming the former. The investigation was supplemented with panel data analysis. Firm-specific factors, including, unit-linked and index-linked liabilities, firm size, investments in property, collective undertakings, bonds and equities, and the ratio of government bonds to corporate bonds and country-specific factors, such as life and non-life market concentration, domestic bond market development, private debt development, household spending, banking concentration, non-performing loans, and CO2 emissions, were found to have an important effect on insurers’ solvency ratios. The novelty of this research lies in the investigation of the connection of solvency ratios with variables that prior studies have not yet explored, such as portfolio asset allocation, the life and non-life insurance market concentration, and unit-linked and index-linked products, via the employment of a battery of traditional and machine enhanced methods. Furthermore, it identifies the relation of solvency ratios with bond market development and investments in collective undertakings. Finally, it addresses the substantial solvency risks posed by the high banking sector concentration to insurers under Solvency II.

This research explores the combined influence of risk management techniques, portfolio diversification, and market timing skills on stock market investment success among individual investors in the United States. Drawing on Modern Portfolio Theory, the Efficient Market Hypothesis, and insights from behavioral finance, it frames strong performance as a risk-adjusted result stemming from consistent and thoughtful strategic decisions, rather than mere pursuit of high returns. Data were gathered through a structured questionnaire completed by 385 active investors, with responses captured on a 5-point Likert scale; the analysis employed SPSS for reliability checks, exploratory factor analysis, and multiple regression to evaluate the hypothesized links. The findings reveal three key drivers with significant positive effects: risk management practices (β = 0.648), diversification (β = 0.683), and timing ability (β = 0.696), highlighting that better results emerge from the coordinated use of careful risk oversight, broad asset spreading, and selective timing adjustments, not from any one approach alone. By integrating traditional finance principles with adaptive and psychological viewpoints, the study fills a notable void in prior work that often treated these elements separately. Ultimately, it provides actionable advice for investors and financial advisors aiming to develop robust strategies suited to the challenges of fast-evolving and unpredictable markets.

Matching stocks to investors based on their risk preferences, like "high suitability" or "moderate risk," can be tricky in portfolio management. This study explores two new methods—the Linguistic Fuzzy Assignment Method (LFAM) and the Absolute Difference Calculation Algorithm (ADCA)—to improve how stocks are assigned across large-cap, mid-cap, and small-cap groups. Using a 6x6 cost matrix built from fuzzy linguistic ratings, these methods pair six stocks with six investors, each with distinct risk preferences. Both approaches produce the same optimal stock assignments, achieving a low total unsuitability score of 2.3875, which shows effective portfolio diversification. These results highlight the methods' ability to handle uncertainty, providing useful insights for financial advisors. MATLAB simulations further confirm the solutions' reliability, indicating potential for use in fluctuating markets.