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Regional Sustainability >

2025 , Vol. 6 >Issue 2: 100214

DOI: https://doi.org/10.1016/j.regsus.2025.100214

Research article

Relationship between environmental performance indices and blockchain-based sustainability-focused companies: Evidence from countries in Europe and America

  • Hussain Mohi-ud-Din QADRI a, b ,
  • Hassnian ALI c, d ,
  • Atta UL MUSTAFA , c, d, *
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  • aMinhaj University Lahore, Lahore, 54770, Pakistan
  • bThe University of Melbourne, Melbourne, 3052, Australia
  • cHamad Bin Khalifa University, Doha, 34110, Qatar
  • dInternational Center for Research in Islamic Economics, Minhaj University Lahore, Lahore, 54770, Pakistan
*E-mail address: (Atta UL MUSTAFA).

Received date: 2024-08-09

  Revised date: 2025-01-02

  Accepted date: 2025-03-14

  Online published: 2025-05-21

Copyright

2666-660X/© 2025 Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Abstract

As the world grapples with increasing environmental challenges, innovative technologies are essential for promoting sustainability and accountability. This study examined the impact of environmental performance indices (EPIs) on the growth and investment trends of blockchain-based sustainability-focused companies in 15 countries (Belgium, Czechia, Denmark, Estonia, Finland, France, Germany, Italy, Norway, Poland, Sweden, Spain, Switzerland, the United Kingdom, and the United States) from Europe and America during 2010-2022. This study used the negative binomial regression model to assess the relationship between EPIs and blockchain-based sustainability-focused companies based on the data from the CrunchBase and EarthData. Results indicated that in ecosystem vitality, national terrestrial biome protection efforts were negatively correlated the formation of blockchain-based sustainability-focused companies, while global terrestrial biome protection efforts and marine protected areas had a positive impact on the formation of these companies and the number of funding rounds. In environmental health, PM2.5 exposure had a positive impact on the number of funding rounds. Conversely, pollutants such as sulfur dioxide (SO2) and ocean plastics deterred the formation of blockchain-based sustainability-focused companies and reduced the number of funding rounds. In climate change performance, adjusted emission growth rate for carbon dioxide (CO2), adjusted emission growth rate for F-gases, and adjusted emission growth rate for black carbon had a significantly positive impact on the formation of blockchain-based sustainability-focused companies. Conversely, adjusted emission growth rate for Nitrous Oxide (N2O) and projected greenhouse gas emissions in 2050 negatively affected the formation of these companies. These findings highlight the dual role of EPIs as driving factors and barriers in the development and investment of blockchain-based sustainability-focused companies in countries from Europe and America.

Key words： Blockchain technology; Environmental performance indices; Blockchain-based sustainability-focused companies; Negative binomial regression model; Europe; America

Cite this article

Hussain Mohi-ud-Din QADRI , Hassnian ALI , Atta UL MUSTAFA . Relationship between environmental performance indices and blockchain-based sustainability-focused companies: Evidence from countries in Europe and America[J]. Regional Sustainability, 2025 , 6(2) : 100214 . DOI: 10.1016/j.regsus.2025.100214

1. Introduction

Blockchain technology plays a transformative role in fields such as renewable energy, by fostering transparent and efficient markets (Li et al., 2023; Popkova et al., 2023). This is complemented by studies suggesting that blockchain-based smart contracts could revolutionize environmental governance, offering new ways to incentivize compliance with environmental regulations (Lee and Khan, 2022; Arshad et al., 2023; Misra et al., 2023). Moreover, the adaptation of blockchain technology in supply chain management introduces the unprecedented levels of traceability and accountability, which is critical in minimizing the environmental footprint of production processes (Bager et al., 2022; Wang and Bai, 2023). By embedding blockchain technology into the core operations of the supply chain, it could potentially catalyze the widespread adoption of greener practices across industries. Additionally, the deployment of blockchain technology in monitoring and trading C credits is a prime example of its application in direct environmental management. This capability could significantly influence the formation and investment in blockchain-based sustainability-focused companies by providing a robust mechanism to ensure that the environmental impact is accurately recorded and managed (Alhasan and Hamdan, 2023; ZiYa and Guo, 2023).
The relationship between blockchain technology and environmental performance indices (EPIs) extends into the realm of corporate social responsibility. Blockchain technology’s ability to ensure transparency can help companies meet the increasing demands of stakeholders for sustainable and ethical operations (Ronaghi and Mosakhani, 2022). This alignment with corporate social responsibility objectives not only bolsters a company’s reputation, but also enhances its overall sustainability, thus attracting more investments focused on green technologies (Corrêa Tavares et al., 2021). It is also crucial to acknowledge the role of EPIs in informing investors and stakeholders about a company’s environmental stewardship. High scores of EPIs indicate that a company’s environmental performance is aligned with Sustainable Development Goals (SDGs), which can attract potential investors looking for ethical investment opportunities (Teh et al., 2020; Parmentola et al., 2022).
Despite the valuable insights into existing literature, significant gaps remain in understanding how the adoption of blockchain technology intersects with sustainability metrics, including EPIs. Most prior studies narrowly focus on specific applications, for instance, C trading, renewable energy markets, or supply chain optimization (Wang and Su, 2020; Alhasan and Hamdan, 2023). While these contributions highlight the potential of blockchain technology, they provide limited empirical evidence on its broader impact on the formation and investment dynamics of blockchain-based sustainability-focused companies. Furthermore, the geographic differences in the adoption of blockchain technology correlated to environmental governance remain underexplored.
Moreover, while prior literature has predominantly focused on the capabilities of blockchain technology, our study demonstrates how blockchain technology can be applied to broader and sustainable corporate strategy and investment. By highlighting the geographic and sectoral disparities in the adoption of blockchain technology, this study provides new empirical evidence on how environmental governance and technological innovation converge to drive sustainability-focused companies.
This study stems from the increasing significance of sustainable business practices and the pivotal role that technological innovations like blockchain technology can enhance environmental governance. We explored the relationship between blockchain-based sustainability-focused companies and EPIs across various countries and examined how these indices impact the formation and investment in such companies. Our research seeks to provide empirical evidence and deeper insights into how blockchain technology can serve as a transformative tool for environmental governance.
We utilized a robust empirical approach to examine the impact of EPIs on the formation and investment dynamics of blockchain-based sustainability-focused companies in 15 countries from Europe and America (Belgium, Czechia, Denmark, Estonia, Finland, France, Germany, Italy, Norway, Poland, Sweden, Spain, Switzerland, the United Kingdom, and the United States). Data were derived from the EarthData and CrunchBase during 2010-2022, which included key financial and operational metrics of over 250,000 companies globally. The selection criteria focused on companies categorized under ‘sustainability’ across 15 distinct categories. We employed a negative binomial regression model to handle the count data associated with the formation of companies and investment activities, considering the overdispersion inherent in such datasets.
The contributions of this study are manifold, providing significant insights into the role of EPIs in shaping the landscape of blockchain-based sustainability-focused companies. Firstly, our research delineates how specific EPIs influence the formation and investment trends of blockchain-based sustainability-focused companies, highlighting a clear link between higher scores of EPIs and more entrepreneurial and investor activity in these companies. This finding enriches the discourse on the integration of SDGs into business and investment decisions, illustrating that environmental governance can align with economic incentives. Secondly, this study extends the academic dialogue on the potential of blockchain technology as a tool for enhancing transparency and accountability in environmental governance. This study finds that blockchain technology can facilitate more stringent adherence to environmental regulations and standards. Lastly, by assessing the impact of EPIs on blockchain-based sustainability-focused companies across different countries, this study contributes to a more detailed understanding of regional disparities in environmental governance and technological adoption.

2. Literature review

Rukhiran et al. (2024) provided substantial evidence supporting the role of blockchain technology as a catalyst for sustainability, highlighting the potential of blockchain technology to improve energy efficiency. Expanding on this, Tawiah et al. (2022) examined the potential of blockchain technology to ensure environmental data accuracy, which is critical for trustworthy EPIs. Santoso et al. (2024) underlined the significance of data integrity and how blockchain technology may assist in avoiding greenwashing technology by creating irreversible records. Meanwhile, Ali et al. (2023) and Dutta and Chakraborty (2024) reviewed how blockchain technology could provide transparent and accountable reporting of sustainable practices that meets stakeholders’ growing expectations of corporate environmental responsibility. Faisal et al. (2024) demonstrated how blockchain technology could introduce a new paradigm for transparency and traceability. Proença et al. (2024) stressed the significance of blockchain technology in improving supply chain resilience to environmental challenges. Al Aina and Faisal (2024) discussed the potential of blockchain technology to encourage sustainable procurement techniques. By investigating blockchain technology’s broader implications for sustainable logistics via the lens of Riedel and Velamuri (2024), we may argue for a shift towards more environmentally friendly supply chain operations. Schneider (2019) argued that the blockchain technology has potentially enabled sustainable supply chain to directly impact a company’s EPIs.
Another intriguing scenario is how blockchain technology is transforming the energy business. Boonsong et al. (2024) explained how blockchain technology can make renewable energy markets more transparent and efficient. Riedel and Velamuri (2024) investigated peer-to-peer energy transactions using blockchain technology, which represented a significant step towards democratizing energy distribution. Wang and Su (2020) studied how blockchain technology was used to effectively monitor renewable energy, ensuring that energy consumption aligns with SDGs. Ali et al. (2023) underscored the role of blockchain technology in decreasing carbon footprints via innovative energy solutions, while Singh et al. (2022) highlighted blockchain technology’s role in improving energy networks.
The literature also emphasizes blockchain technology’s function as a catalyst for environmental governance and corporate social responsibility. Schneider (2019) highlighted how blockchain technology can enhance corporate accountability and transparency in environmental initiatives by empowering stakeholders. Singh et al. (2022) demonstrated how blockchain technology might engage communities in environmental efforts and foster a more inclusive approach to corporate social responsibility. The study of Riedel and Velamuri (2024) showed that blockchain technology has the potential to change environmental governance by increasing stakeholder involvement and oversight. The synthesis of these scholarly investigations demonstrates that blockchain technology is at the forefront of the sustainable development agenda due to its ability to increase transparency, ensure data integrity, and encourage effective and responsible practices in supply chain operations, energy management, and corporate social responsibility. Therefore, this study analyses that how blockchain technology might be utilized to promote the sustainability movement and generate significant environmental benefits.
Yale Center for Environmental Law & Policy and Center for International Earth Science Information Network Earth Institute, Columbia University, ranked the 180 countries based on their EPIs in 2022. To put it another way, EPIs serve as the worldwide standard for assessing how successfully governments are reacting to urgent environmental concerns such as biodiversity loss and climate change, with a focus on sustainability. It assesses a country’s progress towards sustainable practices based on information on ecosystem vitality, environmental health, and climate change performance. Similarly, Morse (2019) underscored the importance of EPIs as an objective measure of ranking the performance of countries in environmental protection, providing them with a clear scorecard for international comparison. In addition, Srebotnjak (2014) further pinpointed the preconditions for EPIs in developing countries and stressed that the development of EPIs was premised upon quantifiable metrics and mechanisms with easy monitoring, and that they should be useful for policy development.
The use of EPIs reflects the efforts by a country to preserve environmental health and promote ecological vitality. This refined approach aligns with the views of Färe Grosskopf and Hernández-Sancho (2004), who believed in the practicability of designing an EPI through a structured approach using Data Envelopment Analysis as a tool to measure good and bad outputs concerning environmental impact. Chakrabartty (2018) further pinpointed that EPIs ought to be employed in the conceptualization of an index-based geometric mean, which does not call for scaling or weighting and assists in providing a slightly improved environmental performance. This methodological improvement may require specific policy intervention, giving access to efforts towards sustainability in a more nuanced manner. Furthermore, Kortelainen (2008) developed a dynamic environmental performance analysis in studying the changes in eco-efficiency and environmental technology, further clarifying the complexity of sustainability measurement and improvement through time.
Such studies make strong calls for the refinement of EPIs and weighting schemes to have a balanced and robust tool in sustainability assessment. Saisana and Saltelli (2010) provided ample detail in a thorough assessment of EPIs’ uncertainty and sensitivity, showing how it hangs on several methodological choices and suggesting there is room for improvement towards a more reliable assessment. Wells Calkins and Balikov (1994) called for the development of the multidimensional EPIs for tracking performance and strategic alerts to senior management. Athanasoglou et al. (2014) adopted the same approach. Rohov et al. (2021) discerned institutional causes that affect the national score of EPIs and found governance quality and financial stability as key contributors that need further strengthening to improve environmental outcomes. This underscores the fact that effective environmental governance requires a multidimensional approach. Sun et al. (2020) focused on energy, economic, and environmental integration in the assessment of sustainability, especially in South Asia, and proved that regional differences are very high in environmental sustainability performance. The improved sustainability index could serve as a criterion to represent the proper ecological and economic status in a country (Marti and Puertas, 2020). Recently, a study by Sarker et al. (2021) pointed to integrating different sustainability indices as an essential element in the new integrated model. Witulski and Dias (2020) tried to find the reliability and validity of the Sustainable Society Index; it was been revealed to stress that statistically powerful indices, in fact, exactly reflect the SDGs, which are not measured on numerous occasions. Kanmani et al. (2020) identified an unsupervised learning framework for evaluation in the field of environmental sustainability with innovative data-driven approach for policy formulation. Bădîrcea et al. (2022) also explored the relationship between financial development and EPIs in Romania, where diversified effects were found for financial markets and institutions on EPIs.
Blockchain technology is developing as a critical component of sustainability, facilitating a paradigm change in environmental and social governance. Blockchain technology has the potential to transform supply chain transparency by removing organizational and technological barriers to sustainability, as shown by the Technology-Organization-Environment Framework (Tornatzky and Fleischer, 1990). This technology supports the transmission of ideas across sectors by maintaining data integrity and fostering stakeholder trust. The use of blockchain technology in sustainability practices is consistent with Network Theory, which highlights the relevance of decentralized networks in encouraging cooperative activities aimed at accomplishing SDGs.
Furthermore, it emphasizes the importance of blockchain technology’s capacity is important to serve as a sustainable strategic resource within the Resource-Based View. Blockchain technology can provide companies with a competitive advantage by increasing operational efficiency and enabling long-term practices that competitors find difficult to replicate, which is a unique and irreplaceable item (Barney, 1991). When used in conjunction with the Triple Bottom Line approach, this technical asset underlines an organization’s dedication to achieve a balance among social well-being, environmental stewardship, and economic growth. The Triple Bottom Line approach, reinforced by blockchain technology, can ensure that enterprises not only focus on profit, but also have a positive impact on society and the environment, which aligns with SDGs. High scores of EPIs demonstrate a country’s commitment to SDGs, which boosts the reputation of the country with stakeholders such as investors, customers, and government agencies (Suchman, 1995). This legitimacy—based on societal norms and values connected to environmental sustainability—becomes critical to attract money. This relationship is further addressed by Stakeholder Theory, which highlights that organizations that handle environmental challenges proactively are better positioned to meet a variety of stakeholder expectations, including those of investors in sustainability-focused fundraising rounds (Freeman, 1984).
Aligning EPIs with stakeholder expectations not only simplifies access to financing, but also strengthens long-term sustainability and stakeholder engagement. Furthermore, the interaction between the Resource Dependency Theory and the Market-Based View highlights the strategic importance of EPIs in getting finance. According to Market-Based View, a company may differentiate itself in the marketplace and attract environmentally conscious consumers and investors by exhibiting strong environmental performance. Simultaneously, Resource Dependency Theory suggests that by attaining outstanding environmental performance, businesses may minimize their dependency on non-renewable resources while also successfully managing external pressures, such as environmental threats and regulatory obligations (Pfeffer and Salancik, 1978). This strategic perspective not only makes a company more desirable for investors looking for sustainable investment opportunities, but also ensures a company’s resilience and adaptability when confronted with environmental difficulties.
Therefore, we can deduce two null hypotheses (H1 and H2) from the above analyses. H1: EPIs have no impact on the formation and growth of blockchain-based sustainability-focused companies; and H2: EPIs have no impact on investments.

3. Materials and methods

The purpose of this study is to find how blockchain technology induces sustainability and how this sustainability adds value to companies, ultimately enhancing their ability to secure substantial investments during funding rounds. This study utilized multiple dependent variables taken from the CrunchBase (https://www.crunchbase.com/) and independent variables collected from the EarthData (https://earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-epi-2022-2022.00#toc-product-summary) during 2010-2022 (Table 1). The CrunchBase contains information on over 250,000 companies worldwide, including their financial details, formation dates, and pre-seed and post-seed funding histories (Liang and Yuan, 2016; Dalle et al., 2017; Ferrati and Muffatto, 2020; Żbikowski and Antosiuk, 2021; Te et al., 2023).
Table 1 Description of variables.
Variable Category Sub-category Unit Transformation
Dependent variable Blockchain technology - Formation of blockchain-based sustainability-focused companies - None
Number of funding rounds - None
Number of lead investors Persons None
Independent variable Ecosystem vitality Biodiversity and habitat National terrestrial biome protection efforts % None
Global terrestrial biome protection efforts % None
Marine protected areas % None
Protected area representativeness index - None
Biodiversity habitat index - None
Species protection index % None
Species habitat index % None
Ecosystem services Tree cover loss % Logarithmic transformation
Grassland loss % Logarithmic transformation
Wetland loss % Logarithmic transformation
Fishery Fish stock status % Logarithmic transformation
Marine trophic index - Logarithmic transformation
Fish caught by trawling % Logarithmic transformation
Acidification Adjusted emission growth rate for sulfur dioxide (SO2) mg/m3 Logarithmic transformation
Adjusted emission growth rate for nitric oxide (NO) mg/m3 Logarithmic transformation
Agriculture Sustainable pesticide use - None
Sustainable nitrogen (N) management index - None
Water resources Wastewater treatment % None
Environmental health Air quality PM2.5 exposure mg/m3 Logarithmic transformation
Household solid fuels Age-standardized DALYs/105 persons Logarithmic transformation
Ozone exposure Age-standardized DALYs/105 persons Logarithmic transformation
Oxynitride (NOx) exposure mg/m3 Logarithmic transformation
SO2 exposure mg/m3 Logarithmic transformation
Carbon monoxide (CO) exposure mg/m3 Logarithmic transformation
Volatile organic compound exposure mg/m3 Logarithmic transformation
Sanitation and drinking water Unsafe sanitation Age-standardized DALYs/105 persons Logarithmic transformation
Unsafe drinking water Age-standardized DALYs/105 persons Logarithmic transformation
Heavy metals Plumbum (Pb) exposure Age-standardized DALYs/105 persons Logarithmic transformation
Waste management Controlled solid waste % None
Recycling % None
Ocean plastics 106 t Logarithmic transformation
Climate change performance - Adjusted emission growth rate for carbon dioxide (CO2) % None
Adjusted emission growth rate for CH4 % None
Adjusted emission growth rate for F-gases % None
Adjusted emission growth rate for Nitrous Oxide (N2O) % None
Adjusted emission growth rate for black carbon % None
Projected greenhouse gas emissions in 2050 Gg CO2-equivalent Logarithmic transformation
Growth rate in CO2 emissions from land cover % None
Greenhouse gas intensity growth rate % None
Greenhouse gas emissions per capita Gg CO2-equivalent Logarithmic transformation

Note: - means no category or no unit. DALYs, disability-adjusted life-years lost rates.

To retrieve the data from the CrunchBase, we used blockchain as a keyword and selected sustainable companies that were divided into 15 categories (Fig. 1). These categories are very comprehensive as they contain information about all the new and old sustainability companies. This study selected a total of 15 countries in Europe and America (Belgium, Czechia, Denmark, Estonia, Finland, France, Germany, Italy, Norway, Poland, Sweden, Spain, Switzerland, the United Kingdom, and the United States) after thorough filtering as most of the countries contain only data for some specific years leaving us with option to filter out these countries as they could generate the biased results.
Fig. 1. Categories of companies with sustainability.
The negative binomial regression model was used in analyzing count data with due regard to the overdispersion factor that is usually rampant in count data. This contrasts with the Poisson regression model, which assumes equality between the mean and variance of the distribution. The negative binomial regression model, on the other hand, has an extra parameter to cover for the over-dispersion, thus giving a better estimate with high reliability, especially in cases where the data are skewed or with outliers. This makes it preferable for use in the case of count data that do not exactly satisfy the assumptions of the Poisson distribution. Several researchers have highlighted the usefulness of the negative binomial regression model in the analysis of count data. For example, Gardner et al. (1995) discussed the usefulness of the negative binomial regression model over the Poisson regression model in handling overdispersed data. They indicated that the extra parameter for the dispersion within the negative binomial regression model gave it the capability of fitting a much wider range of count data more effectively. O’Hara and Kotze (2010) criticized the wide logarithmic transformation of count data for linear regression analysis because they felt it produced biased estimates. Therefore, they advocated models that are inherently more suited to the distributional characteristics of count data, such as the negative binomial regression model, which would therefore avoid the perils of inappropriately transformed data. The flexibility of the negative binomial regression model in fitting count data to a variety of applications and its ability to accommodate overdispersion make the model a better option for such analyses over a range of fields. The flexibility of the model allows it to give more accurate and interpretable results compared to other regression models, such as the mean and variance assumption, where the mean and variance are assumed to be equal. Therefore, based on the previous studies, we concluded that negative binomial regression model is best suited for this study as it perfectly describes the distributional characteristics of count data (Gardner et al., 1995; Liu et al., 2005; El-Basyouny and Sayed, 2006; Ver Hoef and Boveng, 2007; Piza, 2012; Yang and Berdine, 2015).
Based on Table 1, we developed the following equations with 3 dependent variables and 40 independent variables:
E(A|X1)=α1+β1TBNit+β2TBGit+β3MPAit+β4PARit+β5BHVit+β6SPIit+β7SHIit+μ1,
E(A|X2)=α2+β8TCLit+β9GRLit+β10WTLit+μ2,
E(A|X3)=α3+β11FSSit+β12MTIit+β13FTDit+μ3,
E(A|X4)=α4+β14SDAit+β15NXAit+μ4,
E(A|X5)=α5+β16SPUit+β17SNMit+μ5,
E(A|X6)=α6+β18WWTit+μ6,
E(A|X7)=α7+β19PMDit+β20HSFit+β21OZDit+β22NOEit+β23SOEit+β24COEit+β25VOEit+μ7,
E(A|X8)=α8+β26USNit+β27UWDit+μ8,
E(A|X9)=α9+β28PBDit+μ9,
E(A|X10)=α10+β29CSWit+β30RECit+β31OCPit+μ10,
E(A|X11)=α11+β32CDAit+β33CHAit+β34FGAit+β35NDAit+β36BCAit+β37GHNit+β38LCBit+β29GIBit+β40GHPit+μ11,
where E means the expected value of A, conditional on the set of the independent variables X; A represent the formation of blockchain-based sustainability-focused companies, the number of funding rounds, or the number of lead investors; X1-11 are the biodiversity and habitat, ecosystem services, fishery, acidification, agriculture, water resources, air quality, sanitation and drinking water, heavy metals, waste management, and climate change performance, respectively; α1-11 are the intercepts; β1-40 indicate the coefficients of variables; i means the country; t means the time (a); μ1-11 are the error terms; TBN is the national terrestrial biome protection efforts (%); TBG is the global terrestrial biome protection efforts (%); MPA is the marine protected areas (%); PAR is the protected area representativeness index; BHV is the biodiversity habitat index; SPI is the species protection index (%); SHI is the species habitat index (%); TCL is the tree cover loss (%); GRL is the grassland loss (%); WTL is the wetland loss (%); FSS is the fish stock status (%); MTI is the marine trophic index; FTD is the fish caught by trawling (%); SDA is the adjusted emission growth rate for sulfur dioxide (SO2) (mg/m3); NXA is the adjusted emission growth rate for nitric oxide (NO) (mg/m3); SPU is the sustainable pesticide use; SNM is the sustainable nitrogen (N) management index; WWT is the wastewater treatment (%); PMD is the PM2.5 exposure (mg/m3); HSF is the household solid fuels (age-standardized disability-adjusted life-years lost rates (DALYs)/105 persons); OZD is the ozone exposure (age-standardized DALYs/105 persons); NOE is the NOx exposure (mg/m3); SOE is the SO2 exposure (mg/m3); COE is the CO exposure (mg/m3); VOE is the volatile organic compound exposure (mg/m3); USN is the unsafe sanitation (age-standardized DALYs/105 persons); UWD is the unsafe drinking water (Age-standardized DALYs/105 persons); PBD is the Plumbum (Pb) exposure (age-standardized DALYs/105 persons); CSW is the controlled solid waste (%); REC is the recycling (%); OCP is the ocean plastics (106 t); CDA is the adjusted emission growth rate for carbon dioxide (CO2) (%); CHA is the adjusted emission growth rate for CH4 (%); FGA is the adjusted emission growth rate for F-gases (%); NDA is the adjusted emission growth rate for Nitrous Oxide (N2O) (%); BCA is the adjusted emission growth rate for black carbon (%); GHN is the projected greenhouse gas emissions in 2050 (Gg CO2-equivalent); LCB is the growth rate in CO2 emissions from land cover (%); GIB is the greenhouse gas intensity growth rate (%); and GHP is the greenhouse gas emissions per capita (Gg CO2-equivalent).

4. Results

4.1. Relationship between blockchain technology and ecosystem vitality

Table 2 shows the impact of ecosystem vitality on the formation of blockchain-based sustainability-focused companies and the insightful observations across six categories under ecosystem vitality by the negative binomial regression model. National terrestrial biome protection efforts were negatively associated with the formation of blockchain-based sustainability-focused companies, as indicated by a coefficient of -0.523 with a P-value less than 0.05, suggesting that these efforts may deter the formation of such companies. Conversely, global terrestrial biome protection efforts showed a positive relationship with the formation of these companies (β=0.601, P<0.05), indicating that it can encourage the establishment of these companies. Marine protected areas also had a significant positive impact on the formation of blockchain-based sustainability-focused companies (β=0.020, P<0.05), suggesting a nuanced role of marine conservation in promoting these companies. In ecosystem service category, grassland loss was significantly negatively correlated with the formation of blockchain-based sustainability-focused companies (β= -0.039, P<0.05), which aligned with expectations that reduced land degradation may be conducive to developing sustainability-focused initiatives. However, tree cover loss and wetland loss did not show a significant impact on the formation of blockchain-based sustainability-focused companies, possibly due to the complexities of these indices’ effects on sustainability-focused initiatives. Fish caught by trawling had a significant positive association with the formation of blockchain-based sustainability-focused companies (β=0.117, P<0.05), highlighting the potential impact of fishing practices on these companies. In contrast, marine trophic index was negatively correlated with the formation of blockchain-based sustainability-focused companies (β= -0.019, P<0.05), indicating that healthier marine ecosystems might not directly encourage the formation of these companies, possibly due to the complex interactions between marine biodiversity and economic activities. Adjusted emission growth rate for NO showed a significant positive relationship with the formation of blockchain-based sustainability-focused companies (β=0.095, P<0.05), suggesting that countries with increasing NO emissions were more likely to see the emergence of such companies, potentially due to the heightened environmental awareness and efforts. Sustainable N management index showed a positive association with the formation of blockchain-based sustainability-focused companies (β=0.067, P<0.05), highlighting the role of sustainable agriculture practices in fostering the development of these companies. Sustainable pesticide use did not show a significant impact on the formation of blockchain-based sustainability-focused companies, suggesting that the specific practices within sustainable agriculture may have varying effects. Lastly, wastewater treatment was significantly negatively correlated with the formation of blockchain-based sustainability-focused companies (β= -0.026, P<0.05), indicating that improved wastewater management was linked with a higher likelihood of the formation of these companies. The constant and lnalpha values reflected the intrinsic characteristics of the data and model fit, with the lnalpha values indicating the dispersion parameter in the negative binomial regression model, significant in all but biodiversity and habitat. The Akaike Information Criterion (AIC) values offer a measure for the relative quality of the model, suggesting variations in the explanatory power of different environmental issue categories on the formation of blockchain-based sustainability-focused companies.
Table 2 Results of negative binomial regression model between the formation of blockchain-based sustainability-focused companies and ecosystem vitality.
Category Sub-category Biodiversity and habitat Ecosystem services Fishery Acidification Agriculture Water resources
Biodiversity and habitat National terrestrial biome protection efforts -0.523**
(0.069)
Global terrestrial biome protection efforts 0.601**
(0.084)
Marine protected areas 0.020**
(0.005)
Protected area representativeness index -0.001
(0.009)
Biodiversity habitat index -0.005
(0.014)
Species protection index -0.027**
(0.006)
Species habitat index -0.005
(0.013)
Ecosystem
services		 Tree cover loss -0.008
(0.026)
Grassland loss -0.039**
(0.008)
Wetland loss -0.008
(0.009)
Fishery Fish stock status 0.009
(0.011)
Marine trophic index -0.019**
(0.009)
Fish caught by trawling 0.117**
(0.045)
Acidification Adjusted emission growth rate for SO2 -0.064
(0.080)
Adjusted emission growth rate for NO 0.095**
(0.035)
Agriculture Sustainable pesticide use -0.015
(0.009)
Sustainable N management index 0.067**
(0.014)
Water resources Wastewater treatment -0.026**
(0.008)
Constant -6.954** 3.442** -0.400 -2.507 -2.962** 2.399**
(2.285) (0.797) (0.361) (6.583) (0.728) (0.658)
lnalpha -0.165 1.116** 1.223** 1.290** 1.119** 1.262**
(0.278) (0.167) (0.166) (0.163) (0.173) (0.165)
Observations 195 195 195 195 195 195
AIC 500.051 568.975 582.076 585.861 572.436 582.397

Note: The values in parentheses represent standard errors. AIC, Akaike Information Criterion; **, significance at the P<0.05 level.

Table 3 shows the relationship between the number of funding rounds and ecosystem vitality. The analysis was segmented into six distinct categories: biodiversity and habitat, ecosystem services, fishery, acidification, agriculture, and water resources. Each category was investigated through various EPIs to ascertain their impact on the number of funding rounds in these companies. National terrestrial biome protection efforts demonstrated a significant negative association with the number of funding rounds (β= -0.743, P<0.05), suggesting that stronger terrestrial biome protection at national level may lead to the reduction of the number of funding rounds. Conversely, global terrestrial biome protection efforts had a significant positive impact on the number of funding rounds (β=0.865, P<0.05). Additionally, marine protected areas and protected area representativeness index were positively correlated with the number of funding rounds, with the coefficients of 0.038 and 0.034, respectively, both significant at the P<0.05 level, highlighting the potential role of them in attracting funding. Grassland loss was significantly negatively associated with the number of funding rounds (β= -0.045, P<0.05), aligning with the expectation that lower rate of land degradation was associated with an increased likelihood of securing funding. However, tree cover loss and wetland loss did not exhibit a significant impact on the number of funding rounds, suggesting that their impact on these funding rounds may be more complex. Fish stock status had no significant impact on the number of funding rounds. At the same time, marine trophic index showed a non-significant negative correlation with the number of funding rounds. Fish caught by trawling also did not exhibit a significant correlation with the number of funding rounds, suggesting that fishery-related indices may not have a straightforward impact on the number of funding rounds. Adjusted emission growth rate for SO2 was negatively correlated with the number of funding rounds (β= -0.221, P<0.10), whereas adjusted emission growth rate for NO was positively associated with the number of funding rounds (β=0.162, P<0.05), indicating differing effects of these emissions on funding activities. Sustainable pesticide use had a significant negative relationship with the number of funding rounds (β= -0.031, P<0.10), while sustainable N management index had a significant positive impact on the number of funding rounds (β=0.073, P<0.05). Lastly, wastewater treatment was negatively correlated with the number of funding rounds (β= -0.020, P<0.10), indicating that improvements in wastewater management might be associated with the decreased of the number of funding rounds.
Table 3 Results of negative binomial regression model between the number of funding rounds and ecosystem vitality.
Category Sub-category Biodiversity and habitat Ecosystem services Fishery Acidification Agriculture Water resources
Biodiversity and habitat National terrestrial biome protection efforts -0.743**
(0.134)
Global terrestrial biome protection efforts 0.865**
(0.162)
Marine protected areas 0.038**
(0.009)
Protected area representativeness index 0.034**
(0.016)
Biodiversity habitat index 0.009
(0.020)
Species protection index -0.051**
(0.010)
Species habitat index 0.006
(0.022)
Ecosystem services Tree cover loss -0.016
(0.043)
Grassland loss -0.045**
(0.012)
Wetland loss -0.020
(0.017)
Fishery Fish stock status 0.019
(0.026)
Marine trophic index -0.017
(0.013)
Fish caught by trawling 0.124
(0.090)
Acidification Adjusted emission growth rate for SO2 -0.221*
(0.121)
Adjusted emission growth rate for NO 0.162**
(0.063)
Agriculture Sustainable pesticide use -0.031*
(0.017)
Sustainable N management index 0.073**
(0.023)
Water resources Wastewater treatment -0.020*
(0.012)
Constant -14.411** 4.712** -0.565 6.581 -2.423** 2.163**
(3.996) (1.437) (0.551) (8.711) (1.082) (1.002)
lnalpha 1.194** 2.108** 2.185** 2.204** 2.157** 2.256**
(0.224) (0.182) (0.181) (0.180) (0.182) (0.179)
Observations 195 195 195 195 195 195
AIC 448.210 497.015 503.611 502.246 499.718 504.469

Note: The values in parentheses represent standard errors. *, significance at the P<0.10 level; **, significance at the P<0.05 level.

Table 4 shows the relationship between the number of lead investors and ecosystem vitality in blockchain-based sustainability-focused companies. We observed that national terrestrial biome protection efforts had a significant negative impact on the number of lead investors (β= -0.551, P<0.05), suggesting that the heightened national terrestrial biome protection efforts may reduce investor interest, possibly due to perceived constraints on economic activities. Conversely, global terrestrial biome protection efforts were significantly positively correlated with the number of lead investors (β=0.635, P<0.05), indicating that global terrestrial biome protection efforts might attract more lead investors. Moreover, marine protected areas also had a positive impact on the number of lead investors (β=0.034, P<0.05), underscoring the attractiveness of marine conservation to investors. These results highlighted the importance of ecosystem preservation. Moreover, grassland loss was significantly negatively correlated with the number of lead investors (β= -0.042, P<0.05), reinforcing that sustainable management of natural resources was critical for attracting investment. Tree cover loss and wetland loss also exhibited a negative association with the number of lead investors at the P<0.10 level, suggesting that investors were cautious about the ecological footprint of the ventures they supported. The negative coefficient of marine trophic index (β= -0.037, P<0.05) indicated that sustainability in fish stocks was a consideration for investors. In contrast, the positive but non-significant coefficient for fish caught by trawling suggested nuanced investor attitudes towards fishery sustainability. Adjusted emission growth rate for NO was positively associated with the number of lead investors (β=0.177, P<0.05), indicating a possible focus on tackling NO emissions, however, adjusted emission growth rate for SO2 did not have a significant impact on the number of lead investors, suggesting varying investor concerns across different pollutants. Sustainable N management index showed a significant positive relationship with the number of lead investors (β=0.064, P<0.05), highlighting the value placed on sustainable agricultural practices. The non-significant coefficient for sustainable pesticide use suggested that other factors might be more critical in investor decisions within this category. No significant association was observed for wastewater treatment. This suggested that while water resource management is essential, it might not have a significant impact on the number of lead investors.
Table 4 Results of negative binomial regression model between the number of lead investors and ecosystem vitality.
Category Sub-category Biodiversity and habitat Ecosystem services Fishery Acidification Agriculture Water resources
Biodiversity and habitat National terrestrial biome protection efforts -0.551**
(0.125)
Global terrestrial biome protection efforts 0.635**
(0.150)
Marine protected areas 0.034**
(0.009)
Protected area representativeness index 0.004
(0.019)
Biodiversity habitat index 0.027
(0.031)
Species protection index -0.028**
(0.011)
Species habitat index 0.017
(0.026)
Ecosystem services Tree cover loss -0.083*
(0.043)
Grassland loss -0.042**
(0.013)
Wetland loss -0.031*
(0.018)
Fishery Fish stock status 0.033
(0.023)
Marine trophic index -0.037**
(0.016)
Fish caught by trawling 0.110
(0.084)
Acidification Adjusted emission growth rate for SO2 0.026
(0.199)
Adjusted emission growth rate for NO 0.177**
(0.069)
Agriculture Sustainable pesticide use -0.009
(0.019)
Sustainable N management index 0.064**
(0.024)
Water resources Wastewater treatment -0.020
(0.014)
Constant -12.025** 5.925** -0.460 -19.522 -2.808** 2.197*
(4.776) (1.652) (0.599) (18.942) (1.252) (1.144)
lnalpha 1.821** 2.371** 2.402** 2.444** 2.444** 2.531**
(0.221) (0.191) (0.192) (0.189) (0.191) (0.188)
Observations 195 195 195 195 195 195
AIC 441.067 461.606 464.727 464.460 465.547 468.623

Note: The values in parentheses represent standard errors. *, significance at the P<0.10 level; **, significance at the P<0.05 level.

4.2. Relationship between blockchain technology and environmental health

Table 5 shows the impact of environmental health on the formation of blockchain-based sustainability-focused companies. PM2.5 exposure was significantly positively associated with the formation of blockchain-based sustainability-focused companies (β=0.033, P<0.05), suggesting that PM2.5 may spur initiatives to address air pollution through blockchain technology. Conversely, ozone exposure was negatively associated with the formation of blockchain-based sustainability-focused companies (β= -0.032, P<0.05), indicating that ozone pollution might deter the establishment of new ventures in this field. Similarly, SO2 exposure had a negative impact on the formation of these companies (β= -0.060, P<0.05), underscoring the adverse effects of certain pollutants on the entrepreneurial climate for these companies. CO exposure, however, had a marginal positive impact on the formation of these companies (β=0.057, P<0.10). Unsafe sanitation significantly deterred the formation of blockchain-based sustainability-focused companies (β= -0.165, P<0.05), while unsafe drinking water positively associated with the formation of these companies (β=0.139, P<0.05). This dichotomy suggested that challenges in water and sanitation may differently affect the perceived opportunities for the application of blockchain technology in enhancing environmental health. Pb exposure was marginally negatively correlated with the formation of blockchain-based sustainability-focused companies (β= -0.027, P<0.10), reflecting concerns over the health effects of heavy metals and their impact on the inception of sustainability initiatives. Ocean plastics significantly discouraged the formation of blockchain-based sustainability-focused companies (β= -0.053, P<0.05), pointing to the critical concern over plastic pollution and its complex relationship with blockchain technology.
Table 5 Results of negative binomial regression model between the formation of blockchain-based sustainability-focused companies and environmental health.
Category Sub-category Air quality Sanitation and drinking water Heavy metals Waste management
Air quality PM2.5 exposure 0.033**
(0.015)
Household solid fuels 0.029
(0.020)
Ozone exposure -0.032**
(0.016)
NOx exposure -0.076
(0.054)
SO2 exposure -0.060**
(0.018)
CO exposure 0.057*
(0.033)
Volatile organic compound exposure -0.004
(0.012)
Sanitation and drinking water Unsafe sanitation -0.165**
(0.024)
Unsafe drinking water 0.139**
(0.023)
Heavy metal Pb exposure -0.027*
(0.016)
Waste management Controlled solid waste -0.005
(0.050)
Recycling -0.015
(0.011)
Ocean plastics -0.053**
(0.012)
Constant -2.059 2.836** 2.628** 2.214
(1.529) (1.160) (1.296) (4.826)
lnalpha 0.677** 0.607** 1.345** 0.943**
(0.203) (0.221) (0.161) (0.180)
Observations 195 195 195 195
AIC 548.731 541.845 589.318 558.890

Note: The values in parentheses represent standard errors. *, significance at the P<0.10 level; **, significance at the P<0.05 level.

Table 6 shows the impact of environmental health on the number of funding rounds. Specifically, PM2.5 exposure had a marginal positive impact on the number of funding rounds (β=0.049, P<0.10), suggesting that increased awareness and concern over particulate matter pollution may drive investment into blockchain-technology to mitigate air quality issues. Conversely, SO2 exposure was significantly negatively associated with the number of funding rounds (β= -0.063, P<0.05), indicating that SO2 may deter investment, possibly due to the challenges or regulatory complexities associated with addressing such pollution. Within the sanitation and drinking water category, unsafe sanitation had a significant negative impact on the number of funding rounds (β= -0.215, P<0.05). At the same time, there was a strong positive correlation between unsafe drinking water and the number of funding rounds (β=0.215, P<0.05). The analysis of heavy metals and waste management revealed a nuanced landscape. Pb exposure had no significant impact on the number of funding rounds, suggesting that the specific characteristic of heavy metal pollution may not play a central role in shaping funding dynamics for blockchain-based sustainability-focused companies. Ocean plastics had a significant negative impact on the number of funding rounds (β= -0.066, P<0.05), highlighting the critical concern among investors regarding plastic pollution and its impact on environmental sustainability and potential regulatory intervention.
Table 6 Results of negative binomial regression model between the number of funding rounds and environmental health.
Category Sub-category Air quality Sanitation and drinking water Heavy metals Waste management
Air quality PM2.5 exposure 0.049*
(0.027)
Household solid fuels 0.027
(0.036)
Ozone exposure -0.037
(0.023)
NOx exposure -0.172
(0.107)
SO2 exposure -0.063**
(0.030)
CO exposure 0.081
(0.053)
Volatile organic compound exposure -0.005
(0.021)
Sanitation and drinking water Unsafe sanitation -0.215**
(0.043)
Unsafe drinking water 0.215**
(0.040)
Heavy metal Pb exposure -0.023
(0.025)
Waste management Controlled solid waste 0.021
(0.082)
Recycling -0.010
(0.017)
Ocean plastics -0.066**
(0.020)
Constant -2.391 0.516 2.502 0.020
(2.316) (1.938) (2.070) (7.893)
lalpha 1.767** 1.757** 2.285** 1.985**
(0.199) (0.200) (0.178) (0.190)
Observations 195 195 195 195
AIC 484.534 473.646 506.354 490.878

Note: The values in parentheses represent standard errors. *, significance at the P<0.10 level; **, significance at the P<0.05 level.

Table 7 shows the complex dynamics at the intersection of environmental health and the number of lead investors. PM2.5 exposure had a positive impact on the number of lead investors (β=0.058, P<0.05), suggesting that heightened awareness and concern over particulate matter pollution may drive increased investment into companies offering solutions to air quality issues. The negative coefficients for ozone exposure (β= -0.036, P>0.10) and SO2 exposure (β= -0.060, P<0.10) indicated varying sensitivity levels of the number of lead investors to different types of air pollutants, with SO2 exposure notably deterring investor interest. Unsafe sanitation was significantly negatively correlated with the number of lead investors (β= -0.171, P<0.05). At the same time, unsafe drinking water had a significantly positive impact on the number of lead investors (β=0.172, P<0.05). These findings highlighted the divergent effects of water and sanitation challenges on attracting lead investors, emphasizing the critical importance of clean drinking water initiatives in garnering investor support. Ocean plastics notably reduced the number of lead investors (β= -0.057, P<0.05), reflecting growing concern over plastic pollution and its detrimental environmental impact. Conversely, Pb exposure and recycling did not have a statistical significant impact on the number of lead investors, suggesting that the specifics of heavy metal pollution and waste management practices might not be primary considerations for lead investors in blockchain-based sustainability-focused companies.
Table 7 Results of negative binomial regression model between the number of lead investors and environmental health.
Category Sub-category Air quality Sanitation and drinking water Heavy metals Waste management
Air quality PM2.5 exposure 0.058**
(0.029)
Household solid fuels 0.031
(0.039)
Ozone exposure -0.036
(0.026)
NOx exposure -0.182
(0.111)
SO2 exposure -0.060*
(0.034)
CO exposure 0.111*
(0.067)
Volatile organic compound exposure -0.007
(0.025)
Sanitation and drinking water Unsafe sanitation -0.171**
(0.041)
Unsafe drinking water 0.172**
(0.038)
Heavy metals Pb exposure -0.023
(0.028)
Waste management Controlled solid waste 0.087
(0.103)
Recycling -0.016
(0.023)
Ocean plastics -0.057**
(0.024)
Constant -4.945 0.451 2.503 -6.391
(3.109) (2.267) (2.280) (9.849)
lnalpha 2.075** 2.149** 2.555** 2.304**
(0.203) (0.205) (0.187) (0.196)
Observations 195 195 195 195
AIC 453.454 449.669 470.026 459.437

Note: The values in parentheses represent standard errors. *, significance at the P<0.10 level; **, significance at the P<0.05 level.

4.3. Relationship between blockchain technology and climate change performance

Table 8 shows that adjusted emission growth rate for CO2, adjusted emission growth rate for F-gases, and adjusted emission growth rate for black carbon had a significant positive relationship with the formation of blockchain-based sustainability-focused companies. This suggested that a higher emission rate may act as a catalyst, creating businesses that address these specific environmental challenges. Conversely, adjusted emission growth rate for N2O and projected greenhouse gas emissions in 2050 had a negative impact on the formation of blockchain-based sustainability-focused companies, indicating that certain pollutants and long-term emission projections may deter entrepreneurial activity in this field. These findings highlighted the dual role of climate change performance as both a driving factor and a barrier to the formation of blockchain-based sustainability-focused companies.
Table 8 Results of negative binomial regression model between blockchain technology and climate change performance.
Sub-category Formation of blockchain-based sustainability-focused companies Number of funding rounds Number of lead investors
Adjusted emission growth rate for CO2 0.057** 0.122 0.078**
(0.021) (0.220) (0.039)
Adjusted emission growth rate for CH4 0.004 0.028 0.025
(0.010) (0.085) (0.017)
Adjusted emission growth rate for F-gases 0.021** 0.106 0.048**
(0.008) (0.087) (0.017)
Adjusted emission growth rate for N2O -0.020* -0.006 -0.015
(0.012) (0.191) (0.025)
Adjusted emission growth rate for black carbon 0.035** 0.245 0.096**
(0.016) (0.193) (0.049)
Projected greenhouse gas emissions in 2050 -0.030** -0.120 -0.042**
(0.008) (0.092) (0.015)
Growth rate in CO2 emissions from land cover -0.003 -0.049 -0.007
(0.007) (0.073) (0.013)
Greenhouse gas intensity growth rate 0.034 0.345 0.077
(0.023) (0.251) (0.055)
Greenhouse gas emissions per capita -0.039** 0.041 -0.041**
(0.011) (0.091) (0.019)
Constant -7.193** -42.449* -20.350**
(2.106) (24.470) (6.879)
lnalpha 0.592** 4.142** 1.975**
(0.210) (0.160) (0.202)
Observations 195 195 195
AIC 546.779 1717.363 447.493

Note: The values in parentheses represent standard errors. *, significance at the P<0.10 level; **, significance at the P<0.05 level.

These findings were less definitive in the number of funding rounds for companies, suggesting a more complex relationship between climate change performance and financial backing. Adjusted emission growth rate for CO2 was significantly positively correlated with the formation of blockchain-based sustainability-focused companies (β=0.057, P<0.05) and the number of lead investors (β=0.078, P<0.05). Similarly, adjusted emission growth rate for F-gases was significantly positively associated with the formation of blockchain-based sustainability-focused companies (β=0.021, P<0.05) and the number of lead investors (β=0.048, P<0.05). This indicates a potential interest of investors in addressing specific emissions, but also underscores varied investor responses to different environmental challenges. Other indices of climate change performance did not have a significant negative impact on the number of funding rounds further, suggesting that climate change performance did not uniformly affect the decision-making process of investors in the early stages of financial backing.

5. Discussion

This study highlights the intricate relationship between EPIs and blockchain technology, with various factors influencing companies’ formation and investment dynamics. National terrestrial biome protection efforts had a negative association with the formation of blockchain-based sustainability-focused companies, possibly due to perceived operational constraints (Stratopoulos, 2018), while global terrestrial biome protection efforts and marine protected areas had a positive impact on the formation of these companies and the number of funding rounds, reflecting investors’ interest in ecological preservation. PM2.5 exposure and sustainable N management index had a positive impact on the number of funding rounds, indicating that investors preferred environmentally conscious regions. Conversely, pollutants such as SO2 and ocean plastics deterred the formation of blockchain-based sustainability-focused companies and reduced the number of funding rounds, underscoring investor sensitivity to specific environmental challenges. These findings emphasized the critical role of environmental vitality in shaping entrepreneurial and investment landscapes within blockchain technology.
The paradox between national and global terrestrial biome protection efforts highlights different effects on the formation of blockchain-based sustainability-focused companies: national terrestrial biome protection efforts hindered the formation of these companies due to operational constraints, while global terrestrial biome protection efforts fostered the growth of these companies. This contrast underscores the importance of framing and scale in environmental strategies, aligning global terrestrial biome protection efforts with sustainable business practices and company values (Yadav et al., 2016). Furthermore, the negative impact of grassland loss on the formation of blockchain-based sustainability-focused companies and the number of lead investors reveals a critical acknowledgment of the value of ecosystem services. These observations resonate with findings in the financial services sector, where corporate environmental responsibility enhances operational performance, suggesting broader applicability across industries (Jo et al., 2015). The varied impacts of EPIs, such as soil acidification and air pollution, highlight investor sensitivities and challenges facing by blockchain-based sustainability-focused companies (Ardillah, 2019). There is a significant negative relationship of ocean plastics with the formation of blockchain-based sustainability-focused companies and the number of funding rounds, suggesting that there is an urgent need to consider the environmental regulatory aspects of the industry. This concern over ocean plastic pollution reflects a growing awareness and responsiveness to specific environmental challenges, pointing towards a selective sensitivity among entrepreneurs and investors towards certain types of ecological issues. Air quality indices such as PM2.5 exposure, boost the formation of blockchain-based sustainability-focused companies, while other pollutants deter the growth of these companies, highlighting selective investor sensitivities (Jo et al., 2015). Environmental challenges like air pollution and water sanitation shape funding dynamics, highlighting the potential of sustainable investments to drive financial viability (de Souza Cunha et al., 2020).
The pressing need for sustainable solutions to environmental issues is further underscored by the negative association of ocean plastics with the formation of blockchain-based sustainability-focused companies and the number of funding rounds. This concern reflects a broader sensitivity of companies or investors towards the potential environmental impact of specific pollutants, necessitating a specific approach to addressing these challenges within sustainable development and investment (Rana et al., 2019). The interplay between climate change performance and blockchain technology underscores the critical need for a targeted approach to achieving environmental sustainability. This need for specificity in addressing environmental challenges is evident in the varied responses to different pollutants and resources, suggesting that the factors influencing the formation of blockchain-based sustainability-focused companies, the number of funding rounds, and the number of lead investors are complex. This complexity calls for a strategic focus on EPIs that can guide the development of blockchain-based sustainability-focused companies, reinforcing the importance of environmental stewardship in shaping the future of technological innovation and investment (Kölbel et al., 2020).
High scores of EPIs were found to positively affect the entrepreneurial activity and investment patterns in blockchain-based sustainability-focused companies. This result aligns with previous research emphasizing the role of favourable environmental conditions in fostering innovation (Barney, 1991; Hsu and Zomer, 2016). This suggests that companies with the high scores of EPIs benefit from stronger institutional support, public trust, and access to sustainability-focused capital, creating a conducive environment for leveraging blockchain technology. These findings illustrate how blockchain technology can be a transformative tool in achieving the SDGs and contribute to the study of the link between environmental governance and technological innovation (Alhasan and Hamdan, 2023; Arshad et al., 2023). Moreover, EPIs play an important role as a signaling mechanism to attract environmentally conscious investors, thereby promoting the application of blockchain technology in achieving SDGs (Jo et al., 2015; Bădîrcea et al., 2022).
From a theoretical perspective, resource-based view (Barney, 1991) posited that companies gain competitive advantages by effectively utilizing valuable resources such as regulatory frameworks and stakeholder goodwill. High scores of EPIs, in this context, represent a strategic resource that companies can use to attract investment. The ability of blockchain technology to provide transparency, traceability, and accountability further amplifies this advantage (Ronaghi and Mosakhani, 2022; Singh et al., 2022). By embedding environmental stewardship into their core operations, companies operating in regions with high scores of EPIs align themselves with social and institutional expectations, enhancing their long-term viability.
Stakeholder theory also provides a robust framework for interpreting these results (Freeman, 1984). The growing demand for ethical and sustainable business practices among stakeholders underscores the significance of the adoption of blockchain technology in meeting these expectations. Tavares et al. (2021) and Popkova et al. (2023) highlighted the role of blockchain technology in fostering trust and engagement among diverse stakeholder groups. The observed link between high scores of EPIs and increased investment activity suggests that stakeholders perceive companies in high-performing regions as credible partners in advancing environmental goals. This credibility is critical in attracting green investments, which are increasingly guided by environmental, social, and governance criteria (Yadav et al., 2016; de Souza Cunha et al., 2020).
Institutional theory further elucidates the impact of regulatory and normative pressures on the adoption of blockchain technology (Suchman, 1995). Regions with the high scores of EPIs often exhibit stringent environmental policies and a high level of societal awareness, which serve as institutional drivers compelling companies to adopt innovative solutions such as blockchain technology. Ardillah (2019) and Rohov et al. (2021) highlighted the role of governance quality and institutional frameworks in shaping environmental and technological outcomes.
Companies in regions with the high scores of EPIs can align their strategies with regional sustainability priorities and take full advantage of favourable environmental governance. For instance, companies could enhance their value by leveraging blockchain technology for C credit trading, supply chain traceability, and compliance monitoring (Parmentola et al., 2022; Wang and Bai, 2023). Policy-makers, in turn, could facilitate the adoption of blockchain technology by integrating EPIs into investment incentives, thereby creating a virtuous cycle of environmental improvement and technological innovation. These insights extend the contributions of previous studies (Bager et al., 2022; Al Aina and Faisal, 2024) by emphasizing the critical interlinkage among governance, technology, and sustainability.

6. Conclusions and implications

This study, based on the negative binomial regression model and data collected from the CrunchBase and EarthData, analysed the impact of EPIs on the development of blockchain-based sustainability-focused companies in 15 countries (Belgium, Czechia, Denmark, Estonia, Finland, France, Germany, Italy, Norway, Poland, Sweden, Spain, Switzerland, the United Kingdom, and the United States) during 2010-2022. The results indicated that while certain EPIs like global terrestrial biome protection efforts, PM2.5 exposure, and adjusted emission growth rate for CO2 had a positive impact on the formation of blockchain-based sustainability-focused companies, the number of funding rounds, and the number of lead investors, others such as species protection index, ocean plastics, and adjusted emission growth rate for N2O exhibited a negative impact, reflecting the complicated relationship between EPIs and blockchain technology.
This study has several implications for various stakeholders. For companies, the research underscores the importance of aligning business practices with stringent EPIs, which can drive both the formation and investment appeal of blockchain-based sustainability-focused companies. Policy-makers and regulators can craft more effective policies that support the growth of environmentally responsible technologies, while ensuring that these innovations contribute to achieving SDGs. Such policies might include incentives for companies that meet high scores of EPIs or stricter regulations for environmental accountability in the adoption of blockchain technology. For investors, the findings suggest a nuanced approach to funding decisions. Investments in companies with high scores of EPIs could not only yield financial returns, but also contribute to a more sustainable economy, highlighting the importance of EPIs in investment strategies. This study provides a valuable perspective for investors looking to support sustainability through blockchain technology.
Moreover, this study has some limitations. First, potential biases in the CrunchBase may affect the generalizability of the findings, as the dataset may not fully capture the diversity of blockchain-based sustainability-focused companies worldwide. Second, external factors such as political instability, economic conditions, and social attitudes towards technological adoption were not explicitly accounted for, which might affect the observed relationships. Lastly, this study focused on the 15 countries, variations in environmental policies and technological readiness in other countries or regions remain unexplored.
Finally, this research opens new avenues for further investigation into the intersection of technology and environmental governance. The insights gained from the varied effects of specific EPIs on blockchain technology can inform future research, promoting a deeper understanding of how different environmental challenges can be addressed through blockchain technology. For example, this study proposes several directions for future exploration. First, it is possible to analyse the evolution of the relationship between EPIs and the adoption of blockchain technology over a longer period. Second, future research could expand the scope to include additional emerging technologies, such as artificial intelligence or the Internet of Things, which often intersect with blockchain technology in addressing environmental challenges. Third, comparative analyses across a wider array of countries, particularly those in the Global South, could help uncover regional disparities and opportunities in leveraging blockchain for sustainability. Lastly, exploring the mechanisms through which specific EPIs affect the adoption of blockchain technology—such as the role of policy frameworks or industry collaboration—could enrich theoretical understanding of these dynamics.

Authorship contribution statement

Hussain Mohi-ud-Din QADRI: conceptualization and project administration; Hassnian ALI: data curation, formal analysis, and software; and Atta UL MUSTAFA: resources, visualization, and writing - original draft. All authors approved the manuscript.

Declaration of conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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