Low-carbon investment portfolio management: a systematic literature review and future research agenda

Introduction: 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.

Methods: 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.

Results and discussion: 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.

1 Introduction

The growing global concern about reducing greenhouse gas emissions stems primarily from the Paris Agreement, signed in 2015. The agreement seeks to mitigate the impacts of environmental degradation caused by human and industrial activity through international cooperation supported by financial and technological assistance, as well as training and development (UNFCC (United Nations Framework Convention on Climate Change), 2025). This has significantly affected various economic sectors, including production, consumption, and financial investments in sustainability-oriented alternatives.

Low-carbon investments support the energy transition needed to address climate change and promote sustainable economic growth (Chen et al., 2022). Green bonds finance sustainable projects supporting the energy transition, including renewable energy, green buildings, and low-carbon transportation, among others (Verma and Bansal, 2021). Such bonds are issued by governments, financial institutions, and corporations committed to environmental, social, and governance (ESG) issues. Given this context, studies on the management of low-carbon investment portfolios have gained increasing prominence, as researchers seek to understand the associated risks, analytical models, and key criteria.

Monasterolo and De Angelis (2020) conducted a comparative analysis of indices listed on exchanges such as the S&P, Stoxx, FTSE, and Nasdaq. They evaluated the assets using portfolio optimization, CAPM, and the Fama and French model, and found that low-carbon assets were riskier before the Paris Agreement, which later increased investment confidence and attractiveness, lowered beta values, and raised weighting in optimal portfolios. They also found that correlations between low- and high-carbon indices decreased, suggesting greater diversification benefits.

Alessi et al. (2021) analyzed European market equities and identified a risk premium associated with sustainability, indicating that more sustainable assets have lower expected returns, reflecting a “green preference” among investors. Similarly, Monasterolo and De Angelis (2020) applied pricing models such as CAPM and the Fama–French three factor (3FF) model, and incorporated the Cumulative Abnormal Returns (CAR) model into their analysis, using transparency in environmental data disclosure and quantitative measures of carbon emissions as criteria for classifying portfolios into green, brown, and non-green.

Mocanu et al. (2021) analyzed market behavior following the announcement of sustainable bond issuance using linear regression. The authors considered factors such as number of bonds issued, return on assets, social disclosure score, issuance period. The results indicated higher firm value for companies that align financial performance with social responsibility. Furthermore, the study highlighted profitability and the quality of data disclosure as key determinants of the positive performance of the assets analyzed.

Verma and Bansal (2021) investigated the impact of corporate green bond issuances on stock returns in the Indian financial market. They evaluated sectors such as renewable energy, energy efficiency, transportation, and low-carbon buildings, using mean-, market-, and risk-adjusted return models to assess asset price behavior during the 10-day period before and after the announcement of the new bond issuance. The results indicate that although returns were negative on the day of the announcement, they became positive after 10 days, suggesting a favorable investor response and an appreciation of these equities.

Lucchetta (2023) proposed using the Generalized Least Squares (GLS) to assess the effectiveness of climate bonds as a means of transitioning to a zero-emissions economy in countries dependent on fossil fuel sources. The author considered fossil fuel emissions, climate bond issuance, and total fossil fuel consumption, among others, and found that investment in green bond issuance to finance sustainable projects remains limited. Despite growing awareness, emerging countries still rely on other nations for this type of financing.

Alessi et al. (2023) investigated the evolution of the risk premium associated with carbon emissions (the greenium) and companies’ environmental transparency, examining how these factors are priced in the market. They found that investors accept lower bond returns in exchange for holding green and transparent assets, particularly in a context of economic stability.

Akhtaruzzaman et al. (2023) evaluated the inclusion of clean energy funds in investment portfolios and found significantly improved diversification and performance, particularly during periods of high volatility such as the COVID-19 pandemic. They employed models including GARCH-EVT-copula-CVaR, GMV, CET, and minimum variance. According to the authors, diversification strategies based on green assets proved superior to traditional approaches due to their low or negative correlations.

The literature also includes works such as Rahman et al. (2022), mapping the green finance landscape in Bangladesh; Gyamerah and Asare (2024), examining the impact of economic policy on green bonds globally; Rahman et al. (2024), exploring the role of Fintech and sustainable banks; Baştürk (2024), analyzing how green finance influences carbon emissions globally; and Dias et al. (2023), investigating the Web of Science database regarding carbon credits.

In this sense, 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.

Having established the context and aims, this paper presents the systematic literature review process used to investigate the following Research Questions (RQs):

RQ1: What mathematical and statistical methods are used to manage low-carbon investment portfolios?

RQ2: What criteria are used to evaluate low-carbon portfolios?

RQ3: Is there a predominance of specific economic sectors and geographic regions in green bonds managed using the methods identified in RQ1?

RQ4: What are the development trends in low-carbon investment portfolio analysis?

Hence, to answer these questions, the literature review aimed to characterize the investment landscape in low-carbon portfolios based on studies published up to December 2024, identifying evaluation methods, monitoring criteria, predominant investment sectors and regions, and bibliometric indices. This research also enabled us to infer potential research avenues on the topic. This study can be justified by its relevance and importance (Saunders et al. 2007). Relevance lies in how organizations address environmental concerns (e.g., pollution) by aligning their investments with societal demands, while importance is reflected in their commitment to the sustainability goals of the 2030 Agenda and broad interest the topic elicits among researchers and laypeople.

This diagnostic, quantitative study with an exploratory design aimed to examine the context of low-carbon investments. Data were collected through a systematic literature review of databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Page et al. 2021). The literature corpus was analyzed using bibliometric procedures with Zotero software, Python scripts, and a spreadsheet. Finally, a meta-synthesis of the bibliometric results was conducted to facilitate interpretation of the findings and highlight perspectives for new scenarios and future research.

This article is organized into six sections. Following the introduction, the second section describes the theoretical framework and analytical context. The third section presents the methodology and research protocol, while the fourth section discusses the results. The fifth section summarizes the main findings, and the sixth offers the concluding remarks.

2 Materials and methods

A methodological protocol was established for conducting the systematic literature review, ensuring the identification, collection, and analysis of evidence aligned with the study objectives. The PRISMA protocol (Page et al., 2021) was adopted because it provides a structured set of items that guide authors in clearly and comprehensively reporting the methods, results, and conclusions of their reviews. Its use enhances reporting quality, increases transparency, facilitates replication, minimizes bias, and strengthens the communication of systematic reviews.

The research protocol followed the 27 items outlined in PRISMA. These steps, together with the definition of the theme of low-carbon investment portfolios, guided the formulation of the RQs and the establishment of keywords and search strings. Data inclusion and exclusion criteria, as well as eligibility parameters, were also defined. At this stage, the study time frame was determined, and the databases to be consulted were selected.

Once the research theme was defined and the RQs were formulated, three guiding axes were established to identify the keywords for data collection. The first axis comprised the green component (“low carbon,” “green economy,” and “green finance”). The second axis comprised the finance component (“investment portfolio,” “portfolio management,” “stock market,” “carbon credit,” “bond,” and “asset*”). The third axis comprised the mathematical component (“optim*,” “multiobjective,” “multicriteria,” “algorith*,” “simulation*,” “math*,” “statistic*,” and “model*”). Different combinations of these terms were used to retrieve studies, adapting to the search capabilities of each database, while following the syntax “axis 1” AND “axis 2” AND “axis 3,” as illustrated in Table 1. Only research articles published in English were considered for data collection.

Table 1

Table 1. Distribution of studies by search axes and databases.

Table 1 presents the distribution of studies across databases, considering the combination of keywords, the number of studies per database, and the overall collection results. The initial dataset identified yielded a total of 2,059 studies for subsequent classification.

The queries were conducted in four databases available through CAPES Portal de Periódicos: Web of Science, Scopus, ScienceDirect, and IEEE. Data were retrieved on January 28, 2025, and the study time frame spanned from the earliest publications on the topic in these databases until December 31, 2024.

The flowchart in Figure 1 was created following the PRISMA protocol guidelines (Page et al., 2021) and illustrates the filtering steps applied to the initial dataset. It depicts the three main stages of data grouping. The first stage involved the identification of records across databases, during which duplicate items were detected and removed. Some data loss may also occur during import into the processing software. In this study, Zotero (2023) was used, and two studies were excluded due to retraction. After unifying the databases and removing duplicates using Zotero, a CSV file was generated for subsequent analyses.

Figure 1

Figure 1. Flowchart of record identification, screening, and inclusion. Source: Adapted from PRISMA (Page et al., 2021).

After the initial refinement, the second stage, screening, was conducted. In this stage, records were assessed for inclusion or exclusion based on their titles, keywords, and abstracts. The third stage, inclusion, applied the following evaluation criteria: 1. The title contained a keyword aligned with the search terms; 2. The keywords included at least one of the search terms; 3. The abstract referred to at least one investment portfolio analysis criterion or method. These steps determined which studies were included in the final dataset.

Once the studies were included, each article and its characteristics were reviewed to address the initial research question. This step allowed us to extract the data necessary to address the research objective, evaluate the studies, and verify their contributions to the topic. We were also able to understand the state of the art within this context. This process will be detailed in the next section.

3 Results and discussion

The initial set of articles comprised 2,059 articles, which underwent duplicate removal, leaving 1,173 for curation. Several analyses were performed with this dataset, including an examination of publication trends and an assessment of the occurrence of keywords in titles, keywords, and abstracts. The main journals and authors most frequently cited were also identified.

3.1 Initial dataset analysis

The year of publication, keywords, authors, abstracts, and the journals in which the articles were published were considered. Analyses based on the initial set of articles enabled examining publication history. Figure 2 illustrates the growth of scientific production from 1987 to 2024. The number of publications increased over the period, particularly from 2010 onward, with a further increase in 2020, reflecting concerns regarding the evaluation of green investment portfolios in recent years. This upward trend is further supported by the signing of the Paris Agreement in 2015, after which publications on the topic grew significantly.

Figure 2

Figure 2. Temporal distribution of the studies in the initial dataset. Source: Authors’ data.

Another analysis approach was word cloud visualization. To assess whether the retrieved articles aligned with the research topic, we analyzed the words appearing in the keywords, titles, and abstracts.

The dataset was analyzed in Python, employing the pandas, matplotlib, and wordcloud libraries. Word clouds were generated for the titles, abstracts, and keywords, and a Python script was applied to quantify the frequency of terms across these fields.

In the title word cloud, “low carbon” was the most frequent term, followed by “green” and “based.” In the abstract word cloud, the most prominent terms were “low carbon,” “green bond,” and “model.” In the keyword word cloud, “low carbon,” “investment,” “carbon,” and “model” appeared most frequently.

Figures 3a–c illustrate the occurrence of these words. In the word clouds, larger terms represent higher frequency, meaning the more a term appears, the more prominent it becomes in the visualization. This confirms that the search terms are consistent with the initial dataset and aligned with the research theme, thereby supporting the reliability of the data exploration and the resulting literature review.

Figure 3

Figure 3. Word clouds from the final dataset: (a) Titles; (b) abstracts; (c) keywords. Source: Authors’ data.

Another analysis of the initial dataset identified the most representative journals related to this theme. The journals with the largest number of publications during the study period were: Sustainability (Switzerland) – 59 articles; Journal of Cleaner Production – 35; Energy Economics – 25; Environmental Science and Pollution Research – 21; Energy Policy – 21; Construction and Building Materials – 20; Resources Policy – 18; Journal of Molecular Liquids – 15; Applied Energy – 15; and Journal of Environmental Management – 13. Figure 4 shows this distribution ordered by publication count, ranging from 59 to 13 publications. Altogether, these 10 journals accounted for 242 publications, representing an average of 2.11 articles per journal across the dataset (20.59% of the initial dataset).

Figure 4

Figure 4. Journals with the highest number of publications in the initial dataset (source: Authors’ data).

We also identified the most frequently published authors in the initial dataset. The 10 authors with the highest number of publications were: Zarrouk, A. – 7 articles; Lgaz, Hassane – 7; Zhang, Y. – 5; Chen, Y. – 5; Strbac, Goran – 5; Qiu, Jing – 5; Guo, Lei – 5; Lakhrissi, B. – 4; Warad, I. – 4; and Chen, L. – 4. The period analyzed recorded publication counts from 4 to 7 articles, as illustrated in Figure 5.

Figure 5

Figure 5. Authors with the largest number of publications in the initial dataset. Source: Authors’ data.

These analyses confirm alignment with the research objectives, reflecting the growing trend of studies on low-carbon investment portfolios. Likewise, the word clouds of titles, abstracts, and keywords demonstrate consistency with the search terms used to retrieve the dataset. The thematic focus of the journals also shows consistency, with the exceptions of “Construction and Building Materials” and “Journal of Molecular Liquids,” which were evidently captured due to the use of the term “low carbon” in civil engineering and chemistry, respectively. The former is associated with studies on low-carbon materials and steels, while the latter relates to research on fuels and carbon chain structures.

3.2 Data refinement and analysis

After removing duplicates, the classification stage involved reviewing the titles, keywords, and abstracts. Articles with at least one keyword in the title were retained, yielding 560 studies. Next, the articles’ keywords were verified, resulting in 484 retained articles. To further refine the dataset and ensure alignment with the research theme, the abstracts were assessed, and 152 studies were retained. From these, the objectives, methods, criteria, sectors, and countries of origin were identified, leading to a final dataset of 42 articles for full-text reading and analysis of emerging trends in the field.

A total of 42 articles remained for the final assessment and thematic synthesis after refining the initial dataset. Figure 6 presents the temporal distribution of these publications in the final dataset. The studies most closely aligned with the research profile are distributed from 2018 to 2024. A growing trend is evident, consistent with the pattern observed in the initial dataset, indicating that the topic has gained importance in recent years. The number of articles published per year was as follows: 2018–2 articles; 2020–1; 2021–4; 2022–7; 2023–15; and 2024–13 articles.

Figure 6

Figure 6. Temporal distribution of publications in the final dataset. Source: Authors’ data.

To analyze the final dataset, word clouds were generated for the same elements as the initial dataset, enabling comparison and verification of term alignment with the research context. As with the initial dataset, a Python script was used to construct the word clouds for the final dataset.

Figure 7a presents the word cloud for the titles, where the most frequent terms were “green bond,” “evidence,” “market,” and “green.” Figure 7b displays the word cloud for the abstracts, highlighting the terms “green bond,” “market,” “risk,” and “financial,” and Figure 7c shows the word cloud for the keywords, in which the most frequent terms were “market,” “risk,” “energy,” and “carbon.”

Figure 7

Figure 7. Word clouds from the final dataset: (a) Titles; (b) abstracts; (c) keywords. Source: Authors’ data.

As observed in the initial dataset, the terms in the word clouds generated from the titles, abstracts, and keywords also reflect the search terms used in the databases. This confirms the alignment of the terms with the final dataset and their consistency with the research.

In addition, the journals that published the articles in the final dataset were identified, with the following number of publications: Energy Economics – 5 articles; International Review of Financial Analysis – 4; Journal of Environmental Management – 3; Ecological Economics – 2; Journal of Climate Finance – 2; Sustainability – 2; Journal of Financial Stability – 1; Economic Change and Restructuring – 1; Journal of Business Economics and Management – 1; and Vision: The Journal of Business Perspective – 1 (Figure 8).

Figure 8

Figure 8. Journals with the highest number of publications in the final dataset. Source: Authors’ data.

Furthermore, the authors who contributed the most to the final dataset were identified. Among them, Ossola, Alessi, and Panzica, with two articles each, stand out, followed by Teresiene, Chen, Zhao, Kanapickiene, Budriene, Keliuotyte-Staniuleniene, and Verma, as well as the remaining authors, each with one article. Figure 9 presents only 10 of the main authors in the final dataset.

Figure 9

Figure 9. Authors with the highest number of publications in the final dataset. Source: Authors’ data.

From the final dataset of 42 articles, the reading phase began. Next, we proceeded with data extraction, gathering information on the studies’ objectives, the most frequently analyzed sectors, evaluation criteria, methods adopted, locations of the markets studied, and future research directions suggested by the authors.

3.2.1 Studies’ objectives

The studies included in the analysis revealed alignment between the authors’ objectives and the published articles. Although some studies addressed overlapping themes, they were grouped into seven distinct objective categories: impact of COVID-19; risk, volatility and pricing; issuance and market response to green bonds; risk, volatility, and pricing; investment models and strategies; climate policy and transition; market dynamics; and diversification and allocation.

Chen et al. (2022), Ma and Cheok (2023), Jin and Zhang (2023), Tsipas et al. (2024), Zhang et al. (2023), and Arif et al. (2022) examined the effects of the COVID-19 pandemic on green investments, analyzing market volatility, correlations, asset returns, and market response.

Regarding the issuance of and market response to green bonds, Verma and Bansal (2021), Mocanu et al. (2021), Abuzayed and Al-Fayoumi (2022), Abhilash et al. (2023), Lichtenberger et al. (2022), and Shahbaz et al. (2021) examined the market impacts on issuing green assets and assessed the returns on green bonds.

Alessi et al. (2021), Alessi et al. (2023), Mansoux (2024), Aloui et al. (2023), Wang et al. (2024), Wu and Liu (2023), Ye et al. (2024), and Lucchetta (2023) were grouped under risk, volatility, and pricing group. These authors examined the measurement of the risk, volatility, and pricing of green assets and developed models to support this field.

Another group of authors, including Flora and Tankov (2023), Meng and Shaikh (2023), Li et al. (2022b), Marupanthorn et al. (2024), and Argentiero et al. (2022) investigated methods for evaluating and classifying green investments, incorporating ESG criteria and quantitative strategies.

Monasterolo and De Angelis (2020), Gobet and Lage (2024), Monasterolo and Raberto (2018), Ristanović et al. (2024), Prosperi and Zanin (2024), and Dong and Yoon (2023) examined the role of climate policies, analyzing how such policies influence the transition to a low-carbon economy and their financial impacts.

However, Leitão et al. (2021), Li et al. (2022a), Liu et al. (2023), Badía et al. (2024), Basse et al. (2023), Acikgoz (2024), and Sun et al. (2024) focused on studies related to market dynamics. This group examined correlations and interactions between the green and conventional markets, explored carbon and commodity prices, and evaluated emerging market dynamics.

Additionally, Akhtaruzzaman et al. (2023), Ameur et al. (2024) and Wang et al. (2018, 2024) worked in the field of diversification and allocation. These authors examined strategies to improve the performance of green investment portfolios, risk diversification, and employ of alternative assets such as clean energy.

Given the diversity of studies found in the final dataset, it is necessary to map these findings by group. Thus, Table 2 presents the groups identified based on the research topics and their associated authors.

Table 2

Table 2. Classification of studies according to objective categories and authors.

Table 2 shows the grouping of authors and their respective research objectives. Seven groups were identified: impact of COVID-19; issuance and market response to green bonds; risk, volatility, and pricing; investment models and strategies; climate policies; market dynamics; and diversification and allocation.

3.2.2 Sectors

In general, the studies address the stock market as the primary source for analyzing green, low-carbon, and traditional assets. Some establish relationships between sustainable and conventional securities and discuss aspects of green finance and ESG (environmental, social, and governance) investments. Those specifying sectors for evaluation mention renewable energy (solar, wind, and hydro), low-carbon transportation, sustainable buildings, and energy technologies. In addition to these aspects, climate transition policies and CO2 emission reduction, including carbon capture are also addressed. Another aspect is the diversification of investment portfolios, both green and conventional, combined with analysis of investments and divestments, such as fossil fuel assets.

3.2.3 Assessment criteria

The main criteria identified in the studies primarily refer to investment indices listed on the stock exchanges and to the impact of COVID-19. Additional criteria include sustainability and transparency, along with aspects related to asset characteristics and financial sustainability. Risks, uncertainties, strategies, and portfolio performance were also considered.

The financial market has adopted ESG (Environmental, Social, and Governance) criteria for evaluating listed assets. Examples of these indices include the S&P Emerging LargeMidCap ESG Index, the S&P Global 1,200 ESG Index, the S&P Europe 350 ESG Index, and the S&P Global LargeMidCap ESG Index. The inclusion of these indices allows for the evaluation of companies’ performance using updated criteria and for the comparative analysis of assets, both green and conventional securities. Considering the investment indices, the financial returns and risks associated with green assets compared to conventional ones were also assessed, including during the COVID-19 pandemic.

Understanding that several studies have used the pandemic as a temporal and behavioral framework for the market, aspects associated with this context were included in the analysis, such as the number of confirmed cases and deaths caused by the disease. During this period, volatility, acceptance of sustainable investments, and, in particular, abnormal returns on securities were monitored.

Aspects such as the sustainability and transparency of the bond-issuing companies were also relevant factors in the analyses, reflecting the importance of environmental disclosure and carbon emissions. In addition, the classification of portfolios into green and brown (less sustainable) and the resulting impacts on asset prices were highlighted.

3.2.4 Applied methods

To evaluate low-carbon investment portfolios based on these criteria, various techniques were identified, including statistical and econometric analyses, asset pricing models, risk assessment methods, portfolio optimization, multicriteria approaches, network analysis with connectivity, and computational techniques.

Some of the statistical and econometric methods identified include linear regression (simple, bivariate), time series models such as VAR, TVP-VAR, and GARCH, as well as Bayesian models such as TVP-BVAR-SV and MBVAR. Statistical tests such as Newey-West, FMOLS, cointegration, Granger causality, among others, were also used as evaluation tools. Regime-switching models such as Markov-switching and GARCH-MIDAS-Skewed T were likewise applied.

To assess asset pricing, CAPM methods were applied, including extensions that incorporate environmental factors (green factors). Multifactor approaches were also used, such as the 3- and 5-factor Fama–French models, models with a BMG (Brown-Minus-Green) factor, and the CAR model. In addition, factor structure models incorporating risk premiums were employed.

Regarding portfolio optimization and risk assessment models, asset allocation techniques such as minimum variance, equal weighting, GMV, and CET were applied. The Monge–Kantorovich transportation model and the Primal-Dual algorithm were also employed, along with backtesting and portfolio performance evaluation. Additionally, stochastic and deterministic optimization models, including multiscenario optimization and two-stage dynamic programming, stand out.

The multicriteria methods identified include AHP, BWM, and fuzzy WASPAS. Computational techniques such as Wavelet, EEMD, and MFDCCA were also applied, along with simulations such as EIRIN and IAM. In addition, proprietary software like DivFolio in R was developed. Table 3 presents the authors and their respective models and methods, as described in the publications in the dataset.

Table 3

Table 3. Distribution of authors and methods used in the analyzed studies.

3.2.5 Markets analyzed

The studies assessed were authored by researchers from various nationalities. Global analyses are the most prominent, with 24 articles examining assets from companies across multiple countries. The Asian region was represented by 10 articles, focused mainly on the Chinese and Indian markets. The European region appeared in six articles, while the United States was the focus of two articles. Table 4 presents the authors and their respective regions.

Table 4

Table 4. Geographic distribution of studies and respective authors.

3.3 Answers to research questions

RQ1: What mathematical and statistical methods are used to manage low-carbon investment portfolios?

The methods most frequently applied to evaluate low-carbon investment portfolios were mathematical, encompassing a range of statistical and econometric approaches. Linear regression, time series analysis, Bayesian models, and statistical tests were used to examine portfolio behavior. Given the financial nature of the assets, studies also employed specific models for monitoring asset prices, including CAPM, the 3- and 5-factor Fama–French models, the CAR model, and factor structure models with risk premiums. Optimization and risk assessment techniques were common, supported by algorithms such as Primal-Dual and stochastic and deterministic optimization methods. In addition, multicriteria methods (AHP, BWM, fuzzy WASPAS), computational and simulation techniques, and the development of proprietary tools such as DivFolio were reported.

RQ2: What criteria are used to evaluate low-carbon portfolios?

The criteria identified in the articles were primarily linked to stock exchange indices. Some studies also incorporated COVID-19-related aspects as part of their evaluation. ESG factors and information transparency emerged as particularly prominent criteria. Additional aspects included risk assessment, investment returns comparing green and conventional assets, volatility, asset classification into green or less sustainable categories, and the analysis of abnormal returns.

RQ3: Is there a predominance of specific economic sectors and geographic regions in green bonds managed using the methods identified in RQ1?

The findings indicate that the stock market is the primary source of analysis, with a focus on the nature of securities such as green, brown, low-carbon, and traditional assets. Among the studies that address other sectors, the most prominent are renewable energy, low-carbon transportation, sustainable buildings, and related technologies. Investment and divestment in fossil fuels also emerged as recurrent areas of investigation. Researchers are increasingly focused on evaluating low-carbon investment portfolios across companies from diverse countries, making it difficult to identify a single predominant market. Nevertheless, some studies have examined regional markets, with Asia, particularly China and India, receiving the most attention, followed by Europe and the United States.

RQ4: What are the development trends in low-carbon investment portfolio analysis?

The results indicate a growing trend in low-carbon investment portfolios, driven by concerns from social, governmental, and corporate spheres, as well as by the climate crisis and the pursuit of more sustainable energy solutions. For future research, scholars suggest examining the performance of green assets during critical periods, such as wars and pandemics, within specific regions, as well as assessing the effects of energy crises on green and brown assets. They also propose the application of innovative evaluation methods and computational techniques, together with an expansion of observations across diverse markets and regions. In addition, researchers highlight the importance of broadening the scope of asset types to include sovereign, municipal, and corporate bonds. Another emerging direction is the integration of transition costs and climate risk into portfolio optimization, combined with hedging between “clean” and “dirty” assets. Studies also emphasize the relevance of linking green finance with carbon and energy markets. Furthermore, researchers call attention to the need for investigating investor behavior when selecting low-carbon assets, as well as gaps in public policy and regulation and their impacts on risk and return. Finally, aspects related to financial education and awareness of sustainable investment practices are also emphasized.

3.4 Future research

Future research should focus on the financial effects of COVID-19 and the war in Ukraine on green markets, as well as assess the pandemic’s impact on specific sectors and regions. Further studies are also needed to investigate how green markets respond to external shocks such as pandemics and conflicts, along with the effects of energy crises on green and brown assets.

Regarding modeling, the authors suggest applying dynamic models such as DCC-GARC, VAR, and the Kalman filter. Other possibilities include employing machine learning and factor pricing models, as well as incorporating spillover models to evaluate risk transmission in green assets.

Regarding green investment portfolios, the authors recommend expanding analyses beyond China and the European Union to include different types of bonds, such as sovereign, municipal, and corporate bonds. They also propose evaluating the integration of green finance with carbon and energy markets, as well as exploring hedging strategies between “clean” and “dirty” assets. In addition, they emphasize the importance of incorporating physical and climate transition risks into portfolio models and accounting for transition costs and climate risk in portfolio optimization.

Regarding investor behavior, it is important to understand the motivations behind choosing low-carbon assets. In addition, the effects of digitalization on sustainable investment decisions should be examined, along with investor preferences for assets with a green orientation and their perceptions of risk.

Aspects such as public policy and regulation are also mentioned as potential gaps to be addressed. These include assessing the impacts of green finance incentives and policies. Governments should further investigate both the issuance of green assets and the promotion of the sector. In addition, policies targeting low-income and fossil fuel-dependent countries require closer examination. Finally, the effects of public policies on risk and risk-adjusted returns warrant more in-depth analysis.

Another point raised by the authors concerns financial education, awareness, and the broader investment culture, emphasizing the importance of supporting green projects. They recommend investigating sustainable financial education strategies across countries and highlight the need to raise awareness of the risks and benefits of green assets, while exploring ways to encourage investors and companies to adopt sustainable practices.

3.5 Discussion

This study addressed a gap in the literature regarding systematic reviews of low-carbon investment portfolios and the use of mathematical methods as evaluation approaches. It sought to identify the criteria and tools employed in the evaluation of low-carbon investment portfolios and to characterize the regions and the types of assets represented, based on studies published up to December 2024.

To achieve the study objectives, a systematic literature review was conducted in accordance with the PRISMA protocol, using articles retrieved from the Web of Science, Scopus, ScienceDirect, and IEEE databases. The search strategy was structured around three thematic axes—green, financial, and mathematical—, which guided the selection of keywords. Seventeen keyword combinations yielded 2,059 articles indexed up to December 31, 2024. Following the screening process, 42 articles were retained for analysis.

Through bibliometric analysis, the publications were mapped across the study period. The earliest studies date back to 1987, with a marked upward trend from 2010 onward, driven by growing concerns about climate change and reinforced by the signing of the Paris Agreement. The studies in the final dataset fall within the 2018–2024 window. Word clouds generated from both the initial and final datasets were consistent with the research topic, with “green” and “financial” emerging as the most prominent terms. The leading journal identified was Energy Economics, and the most frequently cited authors were Ossola, Alessi, and Panzica.

Regarding research objectives, the most frequently examined topics were risk, volatility, pricing, climate policies, and market dynamics. With respect to sectors, the financial market was the most prominent, while few studies focused on specific investment sectors. The exceptional context of COVID-19 also featured prominently, emerging as a recurrent theme across the analyzed objectives.

The methods identified vary widely, with particular emphasis on mathematical approaches, especially statistical and econometric techniques. The evaluation criteria are broad and largely determined by each researcher, with no clear standardization. Overall, the studies adopt a global perspective on companies, drawing on publicly listed indices and incorporating aspects related to the COVID-19 pandemic, ESG, transparency, and sustainability. In terms of geographical scope, most analyses rely on international data, with a marked focus on the Asian and European markets.

The authors consider as potential gaps the progress in climate policies, the development of more specific criteria for both asset classification and evaluation, and the expansion of evaluation scenarios both geographically and across sectors. They also point to the possibility of applying and developing new methods and models for asset evaluation.

4 Conclusion

This study aimed to identify methods, criteria, sectors, regions, and research gaps in studies on low-carbon investment portfolio management. A systematic literature review was conducted across major databases, combining bibliometric mapping with a synthesis of research on low-carbon investment.

The findings indicate that research on low-carbon investment portfolios has grown steadily since 2010, gaining increasing prominence through December 2024, which underscores the relevance of this study. In addition, the search terms proved consistent with the keywords used in the review.

The authors expanded their focus to include the impacts of COVID-19; issuances and market response to green bonds; risk, volatility, and pricing; investment models and strategies; climate policies; market dynamics; and diversification and allocation. The financial sector emerged as the most prominent area of analysis. Likewise, the main evaluation criteria identified were stock market indices and asset performance. Overall, the analyses were conducted from a global perspective.

Key contributions include mapping the literature on low-carbon investment portfolios in recent years. In addition, the report outlines the main criteria identified in the evaluation, as well as the tools used, sectors, and key future trends in analysis.

This report is organized into six sections. The first is the introduction, providing a brief overview of the article. The second section describes the theoretical framework and the context of the analysis. The third section presents the methodology and protocol of the investigation. The results are then presented and discussed in the fourth section. The fifth section summarizes the findings, and the sixth section discusses the final considerations.

The study recognizes the combination of keywords and the selected time frame as limitations. Future research should broaden the search terms, databases, and time frame to capture additional elements and include data that may have fallen outside the scope of this review. It is also suggested that green cryptocurrencies be evaluated for portfolio diversification, as they were not included in this scope. Furthermore, it suggests potentially expanding the analysis to other sustainable financial markets and instruments.

Author contributions

FB: Writing – original draft, Software, Writing – review & editing, Methodology. AL: Investigation, Writing – original draft, Formal analysis, Supervision, Project administration, Methodology, Writing – review & editing. AD: Validation, Formal analysis, Writing – review & editing, Visualization, Methodology. SE: Formal analysis, Methodology, Validation, Conceptualization, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Acknowledgments

The authors would like to thank the Brazilian National Council for Technological Development (CNPq - Process: 310976/2022-7 - DT level II Researcher Scholarship) for the financial support of the project. This study was partially funded by the Coordination for the Improvement of Higher Education Personnel (CAPES), Brazil – Finance Code 001.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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Publisher’s note

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