Addressing Unprecedented Environmental Challenges: The Role of Entrepreneurship as a Catalyst for Innovation and Sustainable Development in the Age of Climate Change

Addressing Unprecedented Environmental Challenges: The Role of Entrepreneurship as a Catalyst for Innovation and Sustainable Development in the Age of Climate Change

 Asst. Prof. S. Mixillanean Jeromes, Jr.

African Methodist Episcopal University, Liberia

DOI: https://doi.org/10.51244/IJRSI.2025.12040109

Received: 07 April 2025; Accepted: 11 April 2025; Published: 17 May 2025

ABSTRACT

In the face of escalating climate change, humanity confronts a defining challenge marked by rising global temperatures, extreme weather, and environmental degradation. Africa, particularly vulnerable to these realities, finds hope in climate entrepreneurship where innovation meets sustainability. This paper explores the dynamic role of entrepreneurship in addressing environmental issues, emphasizing how startups and SMEs are pioneering renewable energy, climate-smart agriculture, and sustainable waste management.

Using a quantitative research approach, the study analyzes statistical relationships between demographic variables and perceptions of climate entrepreneurship. Descriptive statistics indicated gender-based differences in responses (M = 33, SD = 22.24 for males; M = 42.5, SD = 20.64 for females), though a t-test revealed no significant difference (t(67) = -1.83, p = .072). Several one-way ANOVA tests examined variables such as age, prior participation in climate initiatives, and perceptions of government support. Despite medium to large effect sizes in some cases (e.g., η² = 0.13 for government policy support), no statistically significant differences were found.

These findings suggest that while perceptions and beliefs about climate entrepreneurship vary, they are not strongly divided along demographic lines. Barriers such as limited funding access, unsupportive policies, and market dynamics remain critical. Yet, entrepreneurship remains a powerful tool for innovation and sustainable development. The paper concludes that fostering supportive ecosystems—through policy reform, investment, and education can unlock the full potential of climate-focused entrepreneurship to combat climate change and build resilient communities.

INTRODUCTION

In the era of climate change, humanity faces an existential crisis. Rising global temperatures, melting ice caps, and extreme weather events are no longer distant threats but immediate realities. The United Nations (2022) reports that global temperatures have already risen by 1.1°C above pre-industrial levels, with catastrophic consequences looming if this trend continues. The African continent is no exception to the climate crisis, with rising temperatures, erratic rainfall, and environmental degradation threatening economic stability, food security, and livelihoods. However, despite these challenges, entrepreneurship and investment in sustainable businesses have emerged as powerful drivers of innovation and resilience. From renewable energy projects to climate-smart agriculture and waste management, small and medium enterprises (SMEs) are leading the charge in transforming the world’s economy. Addressing these challenges requires bold, innovative solutions that transcend traditional boundaries, and this is where entrepreneurship steps in as an agent of transformation.

Entrepreneurship has historically been a powerful driver of innovation, economic growth, and societal progress. From the industrial revolution to the digital age, entrepreneurs have disrupted outdated systems and introduced groundbreaking solutions. However, in the context of climate change, entrepreneurship must evolve further integrating sustainability into its core. Entrepreneurs today are uniquely positioned to leverage technology, creativity, and market forces to develop solutions that mitigate environmental harm while promoting sustainable development.

This paper explores the intersection of environmental challenges, entrepreneurship, and innovation, highlighting how entrepreneurs can catalyze sustainable practices, reduce carbon footprints, and revolutionize industries.

The Scope of the Environmental Crisis

The scope of the environmental crisis is vast and complex, posing an existential threat to ecosystems, economies, and human societies. Climate change, driven by greenhouse gas emissions, is at the forefront of this crisis. The Intergovernmental Panel on Climate Change (IPCC, 2021) warns that global temperatures have already risen by 1.1°C above pre-industrial levels, with dire consequences if this trajectory continues. For example, rising sea levels have put over 680 million people living in low-lying coastal areas at risk of flooding and displacement (UN, 2022). In 2021 alone, extreme weather events, exacerbated by climate change, caused $329 billion in economic losses globally, further underlining the urgent need for innovative solutions (Swiss Re, 2022).

Biodiversity loss is another critical aspect of the environmental crisis, with over 1 million species currently at risk of extinction due to habitat destruction, pollution, and climate change (UNEP, 2019). The Amazon rainforest, often referred to as the “lungs of the Earth,” has lost nearly 20% of its original forest cover due to deforestation, threatening its role as a critical carbon sink (WWF, 2021). Entrepreneurs have begun addressing this problem through innovations like drone-based reforestation, pioneered by startups such as BioCarbon Engineering. This technology can plant 100,000 trees per day, offering scalable solutions to restore degraded ecosystems and combat biodiversity loss.

Plastic pollution is yet another alarming dimension of the crisis. An estimated 11 million metric tons of plastic waste enter the oceans annually, endangering marine life and contaminating food chains (Jambeck et al., 2015). Microplastics have been detected in 83% of global tap water samples, posing health risks to humans and animals alike (Orb Media, 2017). Entrepreneurs are stepping up to address this issue, with companies like Loop Industries developing advanced recycling technologies that transform plastic waste into high-quality materials. These examples highlight the magnitude of the environmental crisis and underscore the critical role of entrepreneurship in developing innovative, scalable solutions to mitigate its impact.

Global Environmental Challenges

The scale and urgency of the environmental crisis have reached unprecedented levels, threatening the very foundation of life on Earth. Global temperatures have risen by 1.1°C above pre-industrial levels, resulting in catastrophic consequences such as rising sea levels, melting polar ice caps, and extreme weather events (IPCC, 2021). For example, in 2021 alone, the United States suffered 20 separate billion-dollar weather and climate disasters, costing a total of $145 billion (NOAA, 2022). These events are not just environmental catastrophes but economic shocks that disrupt industries, destroy livelihoods, and exacerbate poverty. The World Bank estimates that without urgent action, climate change could push an additional 132 million people into poverty by 2030. This crisis demands immediate, comprehensive solutions that address both its environmental and socioeconomic dimensions. The Intergovernmental Panel on Climate Change (IPCC, 2021) warns that without drastic emissions reductions, global warming will exceed 2°C within this century, triggering irreversible ecological damage. Challenges include:

Deforestation: The loss of 10 million hectares of forest annually (FAO, 2021).

Biodiversity Loss: Over 1 million species face extinction due to habitat destruction (UNEP, 2019).

Biodiversity loss is another alarming facet of the crisis, with over 1 million species at risk of extinction due to habitat destruction, pollution, and climate change (UNEP, 2019). For instance, coral reefs, which support approximately 25% of marine life, are dying at an alarming rate due to ocean acidification and warming waters. The Great Barrier Reef in Australia has lost nearly 50% of its coral coverage since 1985 (WWF, 2021). This loss of biodiversity disrupts ecosystems, weakens food security, and diminishes natural resources that countless communities depend on. Entrepreneurs have a unique opportunity to address biodiversity loss by developing innovative solutions, such as sustainable farming practices and technologies that reduce deforestation and habitat destruction.

Plastic Pollution is yet another critical challenge, with over 11 million metric tons of plastic entering the oceans annually (Jambeck et al., 2015). This is equivalent to dumping a garbage truck’s worth of plastic into the ocean every minute. The environmental toll is staggering: plastic debris harms marine life, disrupts ecosystems, and even enters the human food chain. For example, microplastics have been found in 83% of global tap water samples (Orb Media, 2017). Entrepreneurs can play a transformative role in addressing plastic pollution by developing alternatives, such as biodegradable materials, and creating circular economy models that encourage recycling and reuse. These examples underscore the urgent need for innovative, entrepreneurial approaches to combat the environmental crisis.

LITERATURE REVIEW

A Ten-Layer Graph Conceptual Model referred to as the “Ten Layer Climate Cake Model” explains the entire concept of “Entrepreneurship as a Catalyst for Innovation and Sustainable Development in the Age of Climate Change” Below is the Ten Layer Climate Cake Model illustrating the interplay between entrepreneurship, innovation, and sustainable development in addressing environmental challenges. Each layer represents a critical component or theory that connects entrepreneurship to climate action. Think of this as a “climate cake,” where every layer builds on the previous one to create a multi-dimensional approach to solving environmental challenges.

Explanation of the Model

Vertical Integration: Each layer builds upon the previous one, creating a comprehensive system where entrepreneurship is the driving force for innovation, collaboration, and sustainability. Theoretical Juxtaposition: The model juxtaposes theories from economics, sociology, and environmental science to provide a multi-disciplinary perspective on climate entrepreneurship. Dynamic Interactions: The layers are interconnected, emphasizing the need for a systems approach to solving climate challenges. For example, innovation (Layer 3) depends on funding (Layer 8) and consumer demand (Layer 6), while global knowledge sharing (Layer 10) amplifies efforts across all layers.

Future Impact

By addressing each of these layers, entrepreneurship can foster a paradigm shift toward a sustainable global economy. The adoption of green technologies, backed by supportive policies and consumer demand, will mitigate environmental challenges and create resilient communities. However, achieving this vision requires collective action, sustained investment, and a commitment to equity and justice.

Visual Representation

Imagine a layered diagram where each layer is a stacked horizontal block. At the bottom is the “Environmental Challenges” layer, forming the foundation, while the “Global Knowledge Sharing” layer crowns the structure, symbolizing the culmination of all efforts. Each layer is color-coded to represent its unique function, and arrows connect the layers to illustrate their interdependence.

Layer 1: Environmental Challenges (The Problem Layer)

This foundational layer identifies the unprecedented global environmental challenges like climate change, biodiversity loss, deforestation, ocean acidification, and plastic pollution. Systems Theory (Ludwig von Bertalanffy, 1968) explains that environmental challenges are interconnected, requiring comprehensive approaches rather than isolated solutions. Future Impact: If ignored, these challenges will lead to climate tipping points, such as the melting of polar ice caps or the collapse of ecosystems, making urgent action imperative.

Layer 2: The Need for Entrepreneurship (The Catalyst Layer)

Entrepreneurship is introduced as a dynamic force capable of driving innovation and creating solutions to environmental issues. Entrepreneurs focus on identifying market opportunities to address systemic problems. Schumpeter’s Theory of Innovation (1934) describes entrepreneurs as “creative destroyers” who disrupt traditional systems to build new, more efficient ones. Future Impact: Climate-focused entrepreneurship will lead to the emergence of green industries, such as carbon capture technologies and renewable energy projects, fostering a new wave of economic growth.

Layer 3: Innovation in Green Technologies (The Engine Layer)

Innovation is the engine that powers entrepreneurial initiatives. This layer encompasses renewable energy, circular economy models, sustainable agriculture, and green manufacturing technologies.

Diffusion of Innovation Theory (Rogers, 1962) explains how new technologies and practices spread across a society. Entrepreneurs are the “early adopters” who champion these innovations.

Future Impact: Accelerating innovation in green technologies will reduce reliance on fossil fuels, lower greenhouse gas emissions, and promote sustainable resource use.

Layer 4: Public-Private Partnerships (The Collaboration Layer)

Public-private partnerships (PPPs) are essential for scaling entrepreneurial efforts. Governments provide policy support and funding, while private companies drive implementation. Stakeholder Theory (Freeman, 1984) emphasizes the importance of collaboration among all stakeholders—governments, businesses, and civil society—to achieve common goals. Future Impact: PPPs will unlock large-scale projects like offshore wind farms and green hydrogen infrastructure, making sustainable solutions more accessible and affordable.

Layer 5: Policy and Regulation (The Governance Layer)

Effective environmental policies and regulatory frameworks play a crucial role in enabling and supporting climate-focused entrepreneurship. Institutional Theory (North, 1990) highlights the role of institutions in shaping economic and social behavior. Clear and supportive policies can incentivize green entrepreneurship. Future Impact: Policies like carbon pricing, tax credits for renewable energy, and subsidies for green startups will create a favorable ecosystem for climate entrepreneurs.

Layer 6: Consumer Behavior and Awareness (The Demand Layer)

This layer focuses on the role of consumers in driving demand for sustainable products and services. Entrepreneurs often rely on consumer behavior to validate their innovations. Key Theory: Behavioral Economics (Kahneman & Tversky, 1979) suggests that consumer decisions are influenced by psychological and social factors. Entrepreneurs can use marketing strategies to nudge consumers toward eco-friendly choices. Future Impact: Increased consumer awareness will create markets for sustainable products, encouraging more entrepreneurs to enter the green economy.

Layer 7: Education and Capacity Building (The Knowledge Layer)

Education systems and capacity-building initiatives are vital for training future entrepreneurs and innovators. This layer emphasizes integrating sustainability into academic and training programs. Human Capital Theory (Becker, 1964) posits that investing in education and skills development enhances economic productivity and innovation. Future Impact is a new generation of climate entrepreneurs who will emerge, equipped with the knowledge and skills to tackle complex environmental challenges.

Layer 8: Financial Mechanisms (The Investment Layer)

Access to funding is a critical enabler for climate entrepreneurship. This layer includes venture capital, green bonds, crowdfunding, and impact investing. Resource-Based View (RBV) (Barney, 1991) argues that financial resources are a key competitive advantage for startups. Future impact will increase investments in green startups, accelerating the commercialization of innovative technologies, creating jobs and reducing environmental degradation.

Layer 9: Social Equity and Inclusion (The Justice Layer)

This layer ensures that entrepreneurial solutions are inclusive and address the needs of marginalized communities most affected by climate change. Environmental Justice Theory (Bullard, 1990) highlights the need to address inequalities in the distribution of environmental benefits and burdens. Future Impact: inclusive entrepreneurship will empower vulnerable communities, ensuring that climate solutions are equitable and socially sustainable.

Layer 10: Global Knowledge Sharing and Collaboration (The Unity Layer)

The final layer emphasizes the importance of international cooperation and knowledge sharing to address global environmental challenges. Globalization Theory (Giddens, 1990) stresses the interconnectedness of nations in tackling shared challenges. Climate entrepreneurship benefits from global networks and best practices. Future Impact, platforms like the United Nations’ Climate Technology Centre and Network (CTCN) will enable entrepreneurs worldwide to access cutting-edge technologies and collaborate on scalable solutions.

The Need for Innovation

Innovation is not just a buzzword in the fight against climate change; it is a necessity. Traditional approaches, such as government regulations and international agreements, have often proven insufficient and fall short due to political inertia and resource constraints. For instance, while the Paris Agreement of 2015 was a landmark accord, global emissions continue to rise, facing criticism for its lack of enforceable mechanisms, with 2021 witnessing a 6% increase in CO2 emissions, the largest annual rise in history (IEA, 2022). This gap between policy commitments and real-world outcomes highlights the need for market-driven, entrepreneurial solutions that can scale rapidly and adapt to changing circumstances. Entrepreneurs, with their ability to think outside the box, are uniquely positioned to bridge this gap by introducing disruptive technologies and sustainable business models.

The renewable energy sector exemplifies how innovation can drive sustainable development. Companies like Tesla have revolutionized the automotive industry by introducing electric vehicles (EVs) that significantly reduce greenhouse gas emissions. Between 2012 and 2022, Tesla’s EVs alone accounted for over 20 million metric tons of CO2 savings (Tesla Impact Report, 2022). Similarly, solar energy companies such as Sunrun and First Solar have made renewable energy more accessible and affordable, helping to shift economies away from fossil fuels. These entrepreneurial ventures not only mitigate environmental harm but also create jobs and stimulate economic growth, proving that sustainability and profitability can go hand-in-hand.

Innovation is also critical in addressing challenges like waste management and resource efficiency. For example, the Dutch company Protix uses insect-based technologies to upcycle organic waste into high-quality protein for animal feed. This not only reduces food waste but also lowers the environmental footprint of traditional feed production. Similarly, the Indian startup Banyan Nation has developed a proprietary process to create high-quality recycled plastic, helping to reduce the reliance on virgin plastic and combat pollution. These examples demonstrate how entrepreneurial innovation can tackle complex environmental issues while creating economic opportunities. By fostering a culture of innovation, societies can transform environmental challenges into avenues for sustainable development.

Entrepreneurship as a Catalyst for Sustainable Development

Climate entrepreneurship is a dynamic force in tackling the global environmental crisis through innovative solutions that align economic growth with sustainability. Entrepreneurs focused on climate challenges, often termed “eco-preneurs,” are disrupting traditional industries by introducing technologies and business models that reduce environmental harm. For instance, the renewable energy sector has seen a massive surge in entrepreneurial activity, with global investments in clean energy reaching $500 billion in 2021, a 27% increase from the previous year (BloombergNEF, 2022). Companies like Tesla have revolutionized the automotive industry with electric vehicles (EVs), reducing dependency on fossil fuels. Tesla, led by Elon Musk, exemplifies how entrepreneurship can disrupt traditional industries. Tesla’s EVs alone saved approximately 5 million metric tons of CO2 emissions in 2021, underscoring the immense potential of climate entrepreneurship to combat global warming (Tesla Impact Report, 2022).

In developing regions, climate entrepreneurship has also proven transformative by addressing both environmental and social challenges. For example, M-KOPA, a solar energy startup in Kenya, has provided affordable, pay-as-you-go solar home systems to over 3 million households in East Africa, replacing kerosene lamps with clean energy sources (M-KOPA, 2021). This innovation not only reduces greenhouse gas emissions but also improves living conditions for low-income families, showcasing how entrepreneurial ventures can foster sustainable development. Similarly, India’s ReNew Power, a renewable energy company, has become one of the largest independent power producers in the world, generating over 13 gigawatts of clean energy annually, enough to power millions of homes sustainably (ReNew Power, 2022).

Climate entrepreneurship extends beyond technology to influence consumer behavior and supply chains. Companies like Beyond Meat and Impossible Foods have disrupted the global food industry by offering plant-based alternatives that require significantly fewer resources. Producing Beyond Meat’s burger, for instance, generates 90% fewer greenhouse gas emissions, 99% less water, and 93% less land use compared to a beef burger (Beyond Meat Sustainability Report, 2021). These examples illustrate that climate entrepreneurship is not only about addressing environmental challenges but also about reshaping industries and inspiring systemic change toward sustainability. Patagonia, the outdoor clothing company, stands as an exemplary model of climate entrepreneurship by integrating sustainability into every aspect of its operations. Known for its commitment to environmental ethics, Patagonia has pioneered initiatives such as its “Worn Wear” program, which promotes the repair and reuse of its products rather than buying new ones. This circular economy model reduces waste and extends the lifecycle of products, addressing the 92 million tons of textile waste generated globally each year (Ellen MacArthur Foundation, 2020). By encouraging customers to repair instead of replace, Patagonia reduces its environmental footprint while fostering a culture of sustainability.

The company also donates 1% of its annual sales to environmental causes, amounting to over $140 million as of 2021 (Patagonia, 2021). In addition, Patagonia is vocal about environmental activism, often using its platform to raise awareness about climate change and advocate for policy reforms. For example, it sued the U.S. government in 2017 to protect national monuments from exploitation, demonstrating how entrepreneurial ventures can extend their impact beyond business operations to influence policy. Patagonia’s leadership in sustainable business practices underscores the potential for entrepreneurship to align profitability with environmental stewardship, inspiring other companies to follow suit.

Major Investments in Africa’s Green Economy

Renewable Energy Investments Powering Africa Sustainably, Africa has abundant solar, wind, and hydro resources, making it a prime destination for renewable energy investments. Lake Turkana Wind Power (Kenya) Investment: $680 million Capacity: 310 MW (provides 15% of Kenya’s electricity) ROI: Estimated at 12-15% annually (African Development Bank, 2021) Climate Impact: Reduces 16 million tons of CO₂ emissions over its lifetime Progress: Fully operational since 2018, contributing to Kenya’s 92% renewable energy mix. Noor Ouarzazate Solar Complex (Morocco) Investment: $2.5 billion, Capacity: 580 MW, ROI: Estimated at 10-14% per year, with cost savings on fossil fuel imports (World Bank, 2022) Climate Impact: Prevents 760,000 tons of CO₂ emissions annually Progress: One of the world’s largest solar plants, expanding Morocco’s renewable energy target to 52% by 2030. M-KOPA Solar (East Africa – Kenya, Uganda, Tanzania, Nigeria), Investment: $200 million ROI: Achieved 30% growth in revenue annually (IFC, 2023), Climate Impact: Replaced 500,000 kerosene lamps, reducing carbon emissions by 1.6 million tons Progress: Over 1.5 million households electrified using a pay-as-you-go solar model

Climate-Smart Agriculture

Securing Africa’s Food Future, with agriculture contributing to 23% of Africa’s GDP, climate change threatens food security and rural livelihoods. However, agri-tech startups are revolutionizing the sector. Twiga Foods (Kenya), Investment: $110 million ROI: 25% annual revenue growth by cutting post-harvest losses (IMF, 2022), Climate Impact: Reduces 40% of food waste by connecting farmers directly to markets Progress: Over 100,000 smallholder farmers integrated into the supply chain Hello Tractor (Nigeria, Kenya, South Africa), Investment: $20 million ROI: Generated $4.5 million in savings for farmers annually (World Economic Forum, 2023) Climate Impact: Decreases soil degradation by 30% through precision farming Progress: Over 75,000 farmers served, with 40% increase in productivity. Farmerline (Ghana), Investment: $14.4 million ROI: Farmers using the platform increase yields by 50% (FAO, 2023), Climate Impact: Provides real-time weather data, helping farmers adapt to erratic rainfall Progress: Over 1.7 million farmers connected across Africa

SMEs Leading Climate Resilience & Circular Economy

Waste Management & Circular Economy, Africa generates over 70 million tons of waste annually, yet only 10% is recycled. SMEs are turning waste into wealth. Wecyclers (Nigeria) Investment: $2 million ROI: Earned 20% annual growth in revenue from waste collection (UNEP, 2022) Climate Impact: Recycled over 10,000 tons of plastic Progress: Provides income for 5,000 households through waste collection.  Gjenge Makers (Kenya) Investment: $1.5 million ROI: 25% profit margins from selling eco-friendly bricks (Forbes Africa, 2023) Climate Impact: Diverted 50 tons of plastic waste monthly from landfills Progress: Produces 1,500 bricks per day, creating employment for women and youth Chanja Datti (Nigeria) Investment: $3 million ROI: 25% increase in revenue annually (World Bank, 2023) Climate Impact: Recycles 1,500 tons of plastic waste annually Progress: Plans expansion to five more African cities by 2025

Water Conservation & Climate Adaptation

With water scarcity affecting 400 million Africans, SMEs are pioneering water-efficient solutions. Safi Water (Kenya) Investment: $1.8 million ROI: 30% revenue growth from clean water sales (Water.org, 2022) Climate Impact: Provides 150,000 liters of clean water per day Progress: Expanded to rural areas, reducing waterborne diseases by 60%.  HydroIQ (Kenya) Investment: $2.2 million ROI: 22% annual growth in smart water metering (TechCrunch, 2023) Climate Impact: Reduced 30% of water leaks in urban supply networks Progress: Over 80,000 liters of water saved per day

How Entrepreneurship Fosters Innovation

Entrepreneurship fosters innovation by driving disruption, enabling scalability, and promoting collaboration; key components in tackling environmental challenges. Disruption occurs when entrepreneurs challenge traditional industries by introducing groundbreaking technologies or business models. For instance, the Dutch company Fairphone has disrupted the electronics industry by creating modular smartphones designed for easy repair and recycling, reducing electronic waste. The United Nations reports that 50 million metric tons of e-waste are generated annually, and Fairphone’s model addresses this growing problem by fostering a circular economy (UNEP, 2019). Scalability is another hallmark of entrepreneurial innovation. Market-driven ventures can rapidly expand their impact, as seen with Sunrun, a U.S.-based residential solar company. Sunrun’s solar installations have grown exponentially, helping over 700,000 households transition to clean energy by 2022 (Sunrun, 2022). Scalability allows entrepreneurs to address climate issues on a large scale, making meaningful contributions to global sustainability goals.

Finally, collaboration amplifies the effectiveness of climate entrepreneurship. Entrepreneurs often partner with governments, NGOs, and private companies to pool resources and expertise. For instance, the Carbon Trust collaborates with startups to commercialize low-carbon technologies, driving innovation in sectors such as energy efficiency and renewable energy. These examples highlight how entrepreneurship leverages disruption, scalability, and collaboration to deliver innovative solutions that combat climate change.

Challenges Facing Climate Entrepreneurship

Despite its potential, climate entrepreneurship faces significant challenges, including limited access to capital, policy barriers, and consumer behavior hurdles. Access to capital is a significant barrier, as clean technologies often require substantial upfront investments. For example, offshore wind energy projects can cost upwards of $3 billion per installation (IRENA, 2021). Many investors are hesitant to fund such ventures due to high risks and long payback periods. However, initiatives like the Green Climate Fund, which has mobilized over $20 billion to finance climate projects, are helping bridge the funding gap for climate entrepreneurs.

Policy barriers also hinder climate entrepreneurship. Inconsistent regulations and subsidies for fossil fuels create an uneven playing field, making it difficult for sustainable ventures to compete. Fossil fuel subsidies totaled $5.9 trillion globally in 2020, effectively undermining the competitiveness of renewable energy solutions (IMF, 2021). Entrepreneurs like those at ReNew Power in India have called for clearer, more supportive policies to drive the transition to green energy. Without such reforms, the growth of climate entrepreneurship remains constrained.

Lastly, consumer behavior poses a challenge, as many people remain hesitant to adopt sustainable products due to higher costs or lack of awareness. For instance, electric vehicles (EVs) face slower adoption rates in regions with insufficient charging infrastructure or where gasoline remains cheaper. However, companies like Tesla are addressing these barriers by building extensive charging networks and reducing EV costs through economies of scale. Overcoming these challenges requires a concerted effort from entrepreneurs, governments, and consumers to create an ecosystem that supports sustainable innovation.

Progress and Future Outlook, Economic Growth and Job Creation. Green investments and SMEs are driving job creation, with the renewable energy sector expected to create over 10 million jobs in Africa by 2030. The circular economy and eco-agriculture sectors are also expanding rapidly, presenting new employment opportunities. Policy Support and Government Initiatives. Many African governments are supporting climate entrepreneurship through initiatives such as: The African Continental Free Trade Area (AfCFTA), which promotes sustainable trade across Africa. The African Development Bank’s Green Growth Initiative, funding clean energy and climate resilience projects. Kenya’s Plastic Ban Policy, which encourages startups to create biodegradable alternatives.

RESEARCH METHODOLOGY

This chapter outlines the research methodology employed to examine the role of entrepreneurship as a catalyst for innovation and sustainable development in addressing unprecedented environmental challenges in the age of climate change. The methodology includes the research design, population, sample size, sampling technique, research instruments and procedures, data analysis and presentation, validity and reliability, and ethical considerations.

Research Method and Design

This study adopts a quantitative research approach to comprehensively analyze the variables. The design is particularly suitable for addressing complex phenomena, such as the intersection of entrepreneurship and environmental sustainability, as it allows for quantitative data and provides a richer understanding of the subject matter (Creswell & Plano Clark, 2017).

Quantitative Component: The quantitative aspect focuses on collecting statistical data through structured surveys distributed to entrepreneurs, environmental policymakers, and stakeholders in green industries. This data provides insights into trends, barriers, and the measurable impacts of entrepreneurial initiatives addressing climate change.

Justification: The Quantitative approach ensures that the study captures numerical trends and the nuanced, contextual information necessary to address the research objectives. With this method, a holistic understanding of how entrepreneurship drives innovation and sustainable development in response to environmental challenges.

Population

The population of this study is 76 individuals including organizations directly involved in or impacted by climate entrepreneurship and environmental sustainability efforts. Specifically, the study focuses on: the various participants drawn from the following;

• Climate Entrepreneurs – Founders and executives of startups and companies in renewable energy, green technology, sustainable agriculture, and circular economy industries.
• Government Officials – Policymakers and regulators from environmental agencies and ministries.
• Environmental Experts: Academics, researchers, and consultants specializing in climate change and sustainability.
• General Public: Consumers and community members who benefit from or engage with climate-focused entrepreneurial initiatives.

Sample Size and Sampling Technique

The sample size for this study was calculated using John Curry’s (1984) Rule of Thumb for Sample Sizes, which guides the determination of representative samples from different population ranges.

Sample Size Calculation

• Climate Entrepreneurs has an estimated population: of 20. Sample size: 100% = 15 participants.
• Government Officials have an estimated population: of 15. Sample size: 100% = 15 participants.
• Environmental Experts is estimated population: 16. Sample size: 100% = 16 participants.

The General Public is estimated to have a population: of 30. Sample size: 100% = 30 participants. Total Sample Size: 76 participants

Sampling Technique

Stratified Random Sampling will be applied to climate entrepreneurs and the general public to ensure proportional representation across sectors and demographics. Purposive Sampling will be used for government officials and environmental experts, as these participants will be selected based on their specialized knowledge and roles relevant to the study. This sampling strategy ensures that the study captures diverse perspectives while maintaining the statistical validity of the findings.

Research Instrument and Procedure

Research Instruments

Survey Questionnaires were structured with closed-ended using Likert-scale questions that were administered to climate entrepreneurs and the general public. Questions focused on the barriers, opportunities, and impacts of entrepreneurial initiatives on environmental sustainability.

Procedure

Pilot Testing of the survey was pre-tested with a small group of participants to ensure clarity, relevance, and reliability. Data Collection: Surveys were distributed electronically. Considering the response rate of 80% with a total of 20 respondents out of the 25 completed the surveys.

Data Analysis and Presentation

Quantitative Data Analysis

Survey responses will be analyzed using descriptive statistics (e.g., mean, median, percentages) to summarize entrepreneurial activity trends and sustainability perceptions. Inferential statistics (e.g., chi-square tests and correlation analysis) will be used to examine relationships between variables, such as the level of government support and the adoption of green technologies. Statistical analysis will be conducted using DATAtab is a modern, web-based statistics software designed for easy data analysis,

Presentation

Quantitative findings are presented in tables and charts.

Validity and Reliability

Validity

Content Validity: The survey questions were reviewed by experts in climate entrepreneurship and sustainability to ensure alignment with the study’s objectives. Quantitative data enhances validity by providing understanding of the research problem (Creswell, 2014).

Reliability

Consistency: The use of standardized instruments ensures consistency in data collection. Pilot Testing: Pre-testing the instruments helped identify and address potential issues, improving reliability.

Ethical Considerations

Informed Consent: All participants received detailed information about the study’s purpose, procedures, and their rights. Written consent was obtained before participation. Participant identities and responses were anonymized, and data was securely stored to protect privacy. Participation will be voluntary, and participants were informed of their right to withdraw at any time without repercussions. The study will avoid sensitive or intrusive questions that could cause discomfort. The research received ethical clearance from the African Methodist Episcopal University’s Ethical Review Board, ensuring compliance with international ethical standards, such as the Belmont Report (1979).

Data Analysis And Presentation

Table 1:  Gender Distribution

		n	Mean	Std. Deviation	Std. Error Mean
Respondent	Male	37	33	22.24	3.66
	Female	32	42.5	20.64	3.65

Field Data Source

The table shows the statistical key figures of the dependent variable Respondent for the two groups Male and Female. Let’s have a look at them:

Sample Size (n): There are 37 sampel values in group Male and 32 sample values in group Female. In classical hypothesis testing (e.g., t-tests), small sample sizes are often considered those under 30.

Mean: The average value of Respondent for the Male group in this sample is 33, while it is 42.5 for the Female group. This shows that the Male group in this sample has on average a lower value for the dependent variable Respondent than the Female group.

Standard deviation: The standard deviation measures the amount of variation or dispersion in a set of values. A low standard deviation means the values are close to the mean, while a high standard deviation means the values are spread out over a wider range. Here, the standard deviation for Male is 22.24 and for Female it is 20.64.

Standard error of the mean: The standard error of the mean estimates the variability of the sample mean if you were to take multiple samples from the same population. A smaller standard error suggests that the sample mean is a more accurate reflection of the population mean.

Note: Note that these are descriptive statistics that only describe the sample data. The descriptive statistics do not tell you whether the differences are statistically significant. For that, you need to look at the results of the t-test below, which tells you whether the differences in means are significant given the sample sizes and variances.

Figure 1

Table 1.1: Assumptions: Tests for normal distribution of Male

	Statistics	p
Kolmogorov-Smirnov	0.1	.81
Kolmogorov-Smirnov (Lilliefors Corr.)	0.1	.429
Shapiro-Wilk	0.94	.043
Anderson-Darling	0.68	.074

This table shows the results of four different statistical tests used to assess whether your data follows a normal distribution. A high p-value (greater than 0.05) suggests that the data does not significantly deviate from normality. If, as in this case, the results of the different tests are contradictory, it is important to take a closer look at the QQ plot. Depending on the field you are researching, different tests are used to test for normal distribution. Therefore, it is best to look at other publications in your field to see which of the tests is mainly used.

Figure 1.1

Table 2: Age Distribution

	n	Mean	Std. Deviation
31 – 40	15	25.93	24.86
21 – 30	39	42.31	19.38
41 -50	9	37.33	22.21
15 – 20	6	34.33	23.31
Total	69	37.41	21.88

The table shows descriptive statistics from a one-way ANOVA analysis for 4 different groups ( 31 – 40, 21 – 30, 41 -50 and 15 – 20 ). Here’s the interpretation of each column in the table. n (Sample Size):This column indicates the number of data points or observations you have for each group. A total of 69 observations means you have data from 69 different instances or subjects, distributed across the 4 groups.

• 31 – 40: 15 observations
• 21 – 30: 39 observations
• 41 -50: 9 observations
• 15 – 20: 6 observations

Mean (Average): The mean value represents the average of all observations in each group. For instance, the average for the 31 – 40 group is 25.93, the average for the 21 – 30 group is 42.31, the average for the 41 -50 group is 37.33 and the average for the 15 – 20 group is 34.33. The overall average, considering all groups, is 37.41.

Std. Deviation (Standard Deviation): Standard deviation measures the amount of variation or dispersion in a set of values. A low standard deviation indicates that the values tend to be close to the mean of the set, while a high standard deviation indicates that the values are spread out over a wider range.

Figure 2:

Table 2.1: Assumptions: Levene test of variance equality

Test	F	df1	df2	p
Levene’s Test (Mean)	0.25	3	65	.86
Brown-Forsythe-Test (Median)	0.16	3	65	.924

This table shows the results of two different tests for equality of variances: Levene’s Test and the Brown-Forsythe Test. Both tests are used to assess whether two or more groups have equal variances, which is an important assumption in many statistical tests like the t-test or ANOVA. Let’s interpret each part of your table:

Levene’s Test (Mean): Levene’s Test using the mean checks if the variances are equal across your groups. A test statistic (F) of 0.25 is obtained, with degrees of freedom 3 and 65. The p-value is .86. Typically, a p-value threshold of 0.05 is used to determine statistical significance. Since .86 is greater than 0.05, this suggests that the test did not find statistically significant evidence to reject the null hypothesis of equal variances. Therefore, you may assume that the variances are equal for the purposes of further analysis like a t-test or an ANOVA.

Brown-Forsythe Test (Median): The Brown-Forsythe Test is a variation of Levene’s Test that is less sensitive to non-normal data distributions since it uses the median. It has a test statistic of 0.16 and a p-value of .924. Typically, a p-value threshold of 0.05 is used to determine statistical significance. This high p-value indicates there is no significant evidence to suggest a violation of the assumption of equal variances. Thus, according to the Brown-Forsythe Test, the variances across the groups can also be considered equal. In summary, both tests suggest that the assumption of equal variances holds for your data.

Tests for normal distribution of 31 – 40

	Statistics	p
Kolmogorov-Smirnov	0.25	.255
Kolmogorov-Smirnov (Lilliefors Corr.)	0.25	.012
Shapiro-Wilk	0.81	.005
Anderson-Darling	1.2	.004

This table shows the results of four different statistical tests used to assess whether the data follows a normal distribution. A high p-value (greater than 0.05) suggests that the data does not significantly deviate from normality. If, as in this case, the results of the different tests are contradictory, it is important to take a closer look at the QQ plot. Depending on the field you are researching, different tests are used to test for normal distribution. Therefore, it is best to look at other publications in your field to see which of the tests is mainly used.

Figure 2.1

Table 3: Have you ever participated in or supported any entrepreneurial initiatives focused on environmental sustainability?

	N	Mean	Std. Deviation
Maybe	5	26.2	16.66
Yes	24	41.92	24.96
No	39	35.95	20.29
Total	68	37.34	21.95

The table shows descriptive statistics from a one-way ANOVA analysis for 3 different groups ( Maybe, Yes and No ). Here’s the interpretation of each column in the table.n (Sample Size): This column indicates the number of data points or observations you have for each group. A total of 68 observations means you have data from 68 different instances or subjects, distributed across the 3 groups.

• Maybe: 5 observations
• Yes: 24 observations
• No: 39 observations

Mean (Average): The mean value represents the average of all observations in each group. For instance, the average for the Maybe group is 26.2, the average for the Yes group is 41.92 and the average for the No group is 35.95. The overall average, considering all groups, is 37.34.

Std. Deviation (Standard Deviation): Standard deviation measures the amount of variation or dispersion in a set of values. A low standard deviation indicates that the values tend to be close to the mean of the set, while a high standard deviation indicates that the values are spread out over a wider range

Figure 3.

Assumptions: Levene test of variance equality

Test	F	df1	df2	p
Levene’s Test (Mean)	3.46	2	65	.037
Brown-Forsythe-Test (Median)	3.47	2	65	.037

This table shows the results of two different tests for equality of variances: Levene’s Test and the Brown-Forsythe Test. Both tests are used to assess whether two or more groups have equal variances, which is an important assumption in many statistical tests like the t-test or ANOVA. Let’s interpret each part of your table:

Levene’s Test (Mean): Levene’s Test using the mean checks if the variances are equal across your groups. A test statistic (F) of 3.46 is obtained, with degrees of freedom 2 and 65. The p-value is .037. Typically, a p-value threshold of 0.05 is used to determine statistical significance. The p-value of .037, which is less than the conventional alpha level of 0.05, suggests that there is statistically significant evidence to reject the null hypothesis of equal variances. This means that the variances of the groups are likely different.

Brown-Forsythe Test (Median): The Brown-Forsythe Test is a variation of Levene’s Test that is less sensitive to non-normal data distributions since it uses the median. It has a test statistic of 3.47 and a p-value of .037. Typically, a p-value threshold of 0.05 is used to determine statistical significance. The p-value of 3.47 is also less than 0.05, indicating statistically significant evidence to reject the null hypothesis of equal variances.

In summary, both Levene’s Test and the Brown-Forsythe Test indicate that the variances in your groups are not equal.

Table 3. 1 Tests for normal distribution of Maybe

	Statistics	p
Kolmogorov-Smirnov	0.26	.818
Kolmogorov-Smirnov (Lilliefors Corr.)	0.26	.434
Shapiro-Wilk	0.93	.62
Anderson-Darling	0.34	.492

This table shows the results of four different statistical tests used to assess whether your data follows a normal distribution. A high p-value (greater than 0.05) suggests that the data does not significantly deviate from normality. All four tests indicate that your data do not deviate significantly from the normal distribution. This means that you can proceed with statistical methods that assume normality of the data. However, it is always a good idea to take a closer look at the QQ plot.

Table 4: What type of entrepreneurial activities do you believe have the greatest impact on environmental sustainability?

	n	Mean	Std. Deviation
Green technology	8	33.13	27.95
Sustainable agriculture	30	35.83	20.59
Renewable energy	17	47.12	22.15
Circular economy (e.g., recycling, upcycling)	12	32.42	18.36
Total	67	37.76	21.82

The table shows descriptive statistics from a one-way ANOVA analysis for 4 different groups ( Green technology, Sustainable agriculture, Renewable energy and Circular economy (e.g., recycling, upcycling) ). Here’s the interpretation of each column in the table.

n (Sample Size): This column indicates the number of data points or observations you have for each group. A total of 67 observations means you have data from 67 different instances or subjects, distributed across the 4 groups.

• Green technology: 8 observations
• Sustainable agriculture: 30 observations
• Renewable energy: 17 observations
• Circular economy (e.g., recycling, upcycling): 12 observations

Mean (Average): The mean value represents the average of all observations in each group. For instance, the average for the Green technology group is 33.13, the average for the Sustainable agriculture group is 35.83, the average for the Renewable energy group is 47.12 and the average for the Circular economy (e.g., recycling, upcycling) group is 32.42. The overall average, considering all groups, is 37.76.

Std. Deviation (Standard Deviation): Standard deviation measures the amount of variation or dispersion in a set of values. A low standard deviation indicates that the values tend to be close to the mean of the set, while a high standard deviation indicates that the values are spread out over a wider range.

Figure 4:

Table 4.1 Assumptions: Levene test of variance equality

Test	F	df1	df2	p
Levene’s Test (Mean)	1.32	3	63	.274
Brown-Forsythe-Test (Median)	1.12	3	63	.348

This table shows the results of two different tests for equality of variances: Levene’s Test and the Brown-Forsythe Test. Both tests are used to assess whether two or more groups have equal variances, which is an important assumption in many statistical tests like the t-test or ANOVA. Let’s interpret each part of your table:

Levene’s Test (Mean): Levene’s Test using the mean checks if the variances are equal across your groups. A test statistic (F) of 1.32 is obtained, with degrees of freedom 3 and 63. The p-value is .274. Typically, a p-value threshold of 0.05 is used to determine statistical significance. Since .274 is greater than 0.05, this suggests that the test did not find statistically significant evidence to reject the null hypothesis of equal variances. Therefore, you may assume that the variances are equal for the purposes of further analysis like a t-test or an ANOVA.

Brown-Forsythe Test (Median): The Brown-Forsythe Test is a variation of Levene’s Test that is less sensitive to non-normal data distributions since it uses the median. It has a test statistic of 1.12 and a p-value of .348. Typically, a p-value threshold of 0.05 is used to determine statistical significance. This high p-value indicates there is no significant evidence to suggest a violation of the assumption of equal variances. Thus, according to the Brown-Forsythe Test, the variances across the groups can also be considered equal. In summary, both tests suggest that the assumption of equal variances holds for your data.

Table 4.2 Tests for normal distribution of Green technology

	Statistics	p
Kolmogorov-Smirnov	0.2	.857
Kolmogorov-Smirnov (Lilliefors Corr.)	0.2	.561
Shapiro-Wilk	0.87	.167
Anderson-Darling	0.49	.22

This table shows the results of four different statistical tests used to assess whether your data follows a normal distribution. A high p-value (greater than 0.05) suggests that the data does not significantly deviate from normality.

All four tests indicate that your data do not deviate significantly from the normal distribution. This means that you can proceed with statistical methods that assume normality of the data. However, it is always a good idea to take a closer look at the QQ plot.

Figure 4.2

Table 5: Do you agree that government policies adequately support climate-focused entrepreneurship?

	n	Mean	Std. Deviation
Strongly disagree	4	53.25	34.97
Strongly agree	14	40.93	28.2
Agree	27	29.7	19.07
Disagree	7	35.43	13.54
Neutral	15	46.27	14.37
Total	67	37.76	21.82

The table shows descriptive statistics from a one-way ANOVA analysis for 5 different groups ( Strongly disagree, Strongly agree, Agree, Disagree and Neutral ). Here’s the interpretation of each column in the table.n (Sample Size): This column indicates the number of data points or observations you have for each group. A total of 67 observations means you have data from 67 different instances or subjects, distributed across the 5 groups.

• Strongly disagree: 4 observations
• Strongly agree: 14 observations
• Agree: 27 observations
• Disagree: 7 observations
• Neutral: 15 observations

Mean (Average): The mean value represents the average of all observations in each group. For instance, the average for the Strongly disagree group is 53.25, the average for the Strongly agree group is 40.93, the average for the Agree group is 29.7, the average for the Disagree group is 35.43 and the average for the Neutral group is 46.27. The overall average, considering all groups, is 37.76.

Std. Deviation (Standard Deviation): Standard deviation measures the amount of variation or dispersion in a set of values. A low standard deviation indicates that the values tend to be close to the mean of the set, while a high standard deviation indicates that the values are spread out over a wider range.

Assumptions: Levene test of variance equality

Test	F	df1	df2	p
Levene’s Test (Mean)	5.09	4	62	.001
Brown-Forsythe-Test (Median)	2.74	4	62	.036

This table shows the results of two different tests for equality of variances: Levene’s Test and the Brown-Forsythe Test. Both tests are used to assess whether two or more groups have equal variances, which is an important assumption in many statistical tests like the t-test or ANOVA. Let’s interpret each part of your table:

Levene’s Test (Mean): Levene’s Test using the mean checks if the variances are equal across your groups. A test statistic (F) of 5.09 is obtained, with degrees of freedom 4 and 62. The p-value is .001. Typically, a p-value threshold of 0.05 is used to determine statistical significance. The p-value of .001, which is less than the conventional alpha level of 0.05, suggests that there is statistically significant evidence to reject the null hypothesis of equal variances. This means that the variances of the groups are likely different.

Brown-Forsythe Test (Median): The Brown-Forsythe Test is a variation of Levene’s Test that is less sensitive to non-normal data distributions since it uses the median. It has a test statistic of 2.74 and a p-value of .036. Typically, a p-value threshold of 0.05 is used to determine statistical significance. The p-value of 2.74 is also less than 0.05, indicating statistically significant evidence to reject the null hypothesis of equal variances.

In summary, both Levene’s Test and the Brown-Forsythe Test indicate that the variances in your groups are not equal.

Table 4.2 Tests for normal distribution of Strongly disagree

	Statistics	p
Kolmogorov-Smirnov	0.41	.396
Kolmogorov-Smirnov (Lilliefors Corr.)	0.41	.016
Shapiro-Wilk	0.71	.015
Anderson-Darling	0.89	.023

This table shows the results of four different statistical tests used to assess whether your data follows a normal distribution. A high p-value (greater than 0.05) suggests that the data does not significantly deviate from normality.

If, as in this case, the results of the different tests are contradictory, it is important to take a closer look at the QQ plot. Depending on the field you are researching, different tests are used to test for normal distribution. Therefore, it is best to look at other publications in your field to see which of the tests is mainly used.

Figure 4.2

Table 6: How accessible is funding for climate entrepreneurship in your region?

	n	Mean	Std. Deviation
Very inaccessible	14	40.36	22.79
Very accessible	15	37.07	22.11
Neutral	15	30.67	22.07
Somewhat accessible	16	40.56	22.09
Somewhat inaccessible	7	42.86	20.56
Total	67	37.76	21.82

The table shows descriptive statistics from a one-way ANOVA analysis for 5 different groups ( Very inaccessible, Very accessible, Neutral, Somewhat accessible and Somewhat inaccessible ). Here’s the interpretation of each column in the table.

n (Sample Size): This column indicates the number of data points or observations you have for each group. A total of 67 observations means you have data from 67 different instances or subjects, distributed across the 5 groups.

• Very inaccessible: 14 observations
• Very accessible: 15 observations
• Neutral: 15 observations
• Somewhat accessible: 16 observations
• Somewhat inaccessible: 7 observations

Mean (Average): The mean value represents the average of all observations in each group. For instance, the average for the Very inaccessible group is 40.36, the average for the Very accessible group is 37.07, the average for the Neutral group is 30.67, the average for the Somewhat accessible group is 40.56 and the average for the Somewhat inaccessible group is 42.86. The overall average, considering all groups, is 37.76.

Std. Deviation (Standard Deviation): Standard deviation measures the amount of variation or dispersion in a set of values. A low standard deviation indicates that the values tend to be close to the mean of the set, while a high standard deviation indicates that the values are spread out over a wider range.

Figure 6

Table 6.1: Assumptions: Levene test of variance equality

Test	F	df1	df2	p
Levene’s Test (Mean)	0.14	4	62	.967
Brown-Forsythe-Test (Median)	0.14	4	62	.967

This table shows the results of two different tests for equality of variances: Levene’s Test and the Brown-Forsythe Test. Both tests are used to assess whether two or more groups have equal variances, which is an important assumption in many statistical tests like the t-test or ANOVA. Let’s interpret each part of your table:

Levene’s Test (Mean): Levene’s Test using the mean checks if the variances are equal across your groups. A test statistic (F) of 0.14 is obtained, with degrees of freedom 4 and 62. The p-value is .967. Typically, a p-value threshold of 0.05 is used to determine statistical significance. Since .967 is greater than 0.05, this suggests that the test did not find statistically significant evidence to reject the null hypothesis of equal variances. Therefore, you may assume that the variances are equal for the purposes of further analysis like a t-test or an ANOVA.

Brown-Forsythe Test (Median): The Brown-Forsythe Test is a variation of Levene’s Test that is less sensitive to non-normal data distributions since it uses the median. It has a test statistic of 0.14 and a p-value of .967. Typically, a p-value threshold of 0.05 is used to determine statistical significance. This high p-value indicates there is no significant evidence to suggest a violation of the assumption of equal variances. Thus, according to the Brown-Forsythe Test, the variances across the groups can also be considered equal.

In summary, both tests suggest that the assumption of equal variances holds for your data.

Table 6.1: Tests for normal distribution of Very inaccessible

	Statistics	p
Kolmogorov-Smirnov	0.15	.855
Kolmogorov-Smirnov (Lilliefors Corr.)	0.15	.55
Shapiro-Wilk	0.94	.417
Anderson-Darling	0.32	.529

This table shows the results of four different statistical tests used to assess whether your data follows a normal distribution. A high p-value (greater than 0.05) suggests that the data does not significantly deviate from normality. All four tests indicate that your data do not deviate significantly from the normal distribution. This means that you can proceed with statistical methods that assume normality of the data. However, it is always a good idea to take a closer look at the QQ plot.

Figure 6

SUMMARY, CONCLUSION, AND RECOMMENDATION AND

Summary

Descriptive statistics: The results of the descriptive statistics showed that the Male group had lower values for the dependent variable Respondent (M = 33, SD = 22.24) than the Female group (M = 42.5, SD = 20.64).

Levene-Test: The Levene test of equality of variance yielded a p-value of .611, which is above the 5% significance level. The Levene test was therefore not significant and the null hypothesis that all variances of the groups are equal was not rejected. Thus, there was variance equality in the samples.

t-test for independent samples: A two tailed t-test for independent samples (equal variances assumed) showed that the difference between Male and Female with respect to the dependent variable Respondent was not statistically significant, t(67) = -1.83, p = .072, 95% confidence interval [-19.87, 0.87]. Thus, the null hypothesis was not rejected.

Effect size: The effect size d was 0.44 (equal variances assumed). With d = 0.44 there was a small effect.

Analysis of variance: A one-way analysis of variance has shown that there was no significant difference between the categorical variable Age and the variable Respondent F = 2.17, p = .1 Thus, with the available data, the null hypothesis was not rejected.

Effect size: The eta squared (η2) value is 0.09, representing the proportion of the variance in the dependent variable that is attributable to the treatment effect. In this context, 9.11% of the variance in the dependent variable can be explained by the differences between the levels of the treatment. The eta squared value of 0.09 suggests a medium to large effect size.

According to Cohen (1988), the limits for eta squared (η2) are .01 (small effect), .06 (medium effect), and .14 (large effect).

f2	Classification according to Cohen (1988)
0.02	weak effect
0.15	moderate effect
0.35	strong effect

The ANOVA showed that there was no significant difference, so it is not reasonable to compute a post hoc test.

Analysis of variance: A one-way analysis of variance has shown that there was no significant difference between the categorical variable Have you ever participated in or supported any entrepreneurial initiatives focused on environmental sustainability? and the variable Respondent F = 1.25, p = .292 Thus, with the available data, the null hypothesis was not rejected.

Effect size: The eta squared (η2) value is 0.04, representing the proportion of the variance in the dependent variable that is attributable to the treatment effect. In this context, 3.71% of the variance in the dependent variable can be explained by the differences between the levels of the treatment. The eta squared value of 0.04 suggests a small to medium effect size.

According to Cohen (1988), the limits for eta squared (η2) are .01 (small effect), .06 (medium effect), and .14 (large effect).

f2	Classification according to Cohen (1988)
0.02	weak effect
0.15	moderate effect
0.35	strong effect

The ANOVA showed that there was no significant difference, so it is not reasonable to compute a

Analysis of variance: A one-way analysis of variance has shown that there was no significant difference between the categorical variable What type of entrepreneurial activities do you believe have the greatest impact on environmental sustainability? and the variable Respondent F = 1.51, p = .219 Thus, with the available data, the null hypothesis was not rejected.

Effect size: The eta squared (η2) value is 0.07, representing the proportion of the variance in the dependent variable that is attributable to the treatment effect. In this context, 6.73% of the variance in the dependent variable can be explained by the differences between the levels of the treatment. The eta squared value of 0.07 suggests a medium effect size. According to Cohen (1988), the limits for eta squared (η2) are .01 (small effect), .06 (medium effect), and .14 (large effect).

f2	Classification according to Cohen (1988)
0.02	weak effect
0.15	moderate effect
0.35	strong effect

The ANOVA showed that there was no significant difference, so it is not reasonable to compute a post hoc test.

Analysis of variance: A one-way analysis of variance has shown that there was no significant difference between the categorical variable Do you agree that government policies adequately support climate-focused entrepreneurship? and the variable Respondent F = 2.24, p = .074 Thus, with the available data, the null hypothesis was not rejected.

Effect size: The eta squared (η2) value is 0.13, representing the proportion of the variance in the dependent variable that is attributable to the treatment effect. In this context, 12.65% of the variance in the dependent variable can be explained by the differences between the levels of the treatment. The eta squared value of 0.13 suggests a large effect size.

According to Cohen (1988), the limits for eta squared (η2) are .01 (small effect), .06 (medium effect), and .14 (large effect).

f2	Classification according to Cohen (1988)
0.02	weak effect
0.15	moderate effect
0.35	strong effect

The ANOVA showed that there was no significant difference, so it is not reasonable to compute a post hoc test.

Analysis of variance: A one-way analysis of variance has shown that there was no significant difference between the categorical variable How accessible is funding for climate entrepreneurship in your region? and the variable Respondent F = 0.6, p = .667 Thus, with the available data, the null hypothesis was not rejected.

Effect size: The eta squared (η2) value is 0.04, representing the proportion of the variance in the dependent variable that is attributable to the treatment effect. In this context, 3.7% of the variance in the dependent variable can be explained by the differences between the levels of the treatment. The eta squared value of 0.04 suggests a small to medium effect size. According to Cohen (1988), the limits for eta squared (η2) are .01 (small effect), .06 (medium effect), and .14 (large effect).

f2	Classification according to Cohen (1988)
0.02	weak effect
0.15	moderate effect
0.35	strong effect

The ANOVA showed that there was no significant difference, so it is not reasonable to compute a post hoc test.

Conclusion

Entrepreneurship is emerging as a powerful force in addressing unprecedented environmental challenges, offering innovative solutions that align economic growth with sustainability. From Tesla’s electric vehicles, which saved over 20 million tons of CO2 emissions between 2012 and 2022, to M-KOPA’s solar systems, which have connected over 3 million off-grid homes to clean energy in East Africa, climate entrepreneurship is redefining industries and empowering communities (Tesla Impact Report, 2022; M-KOPA, 2021). These ventures demonstrate that entrepreneurship is not merely a business endeavor but a catalyst for systemic change, driving innovation in renewable energy, waste management, and sustainable agriculture.

However, the journey to a sustainable future is fraught with challenges that require collective action. Limited access to capital, inconsistent policies, and consumer behavior hurdles continue to constrain the growth of climate entrepreneurship. For instance, global fossil fuel subsidies, which totaled $5.9 trillion in 2020, undermine the competitiveness of renewable energy solutions (IMF, 2021). Addressing these barriers will necessitate increased investments, supportive policies, and behavioral shifts toward sustainability. Public-private partnerships and global knowledge-sharing initiatives can further amplify the impact of entrepreneurial ventures, enabling them to tackle environmental challenges at scale.

In conclusion, climate entrepreneurship represents a beacon of hope in the age of climate change. By fostering a culture of innovation and collaboration, entrepreneurs can transform environmental crises into opportunities for sustainable development. The urgency of the climate crisis demands bold action, and entrepreneurship offers the ingenuity, adaptability, and drive needed to chart a greener, more resilient path forward.

Future Directions

To fully realize the potential of entrepreneurship in addressing environmental challenges, there is an urgent need to ramp up investments in green startups. Despite the growing awareness of climate issues, funding remains disproportionately low. According to BloombergNEF (2022), global venture capital investments in climate tech startups reached $53.7 billion in 2021, a record high but still a fraction of the trillions needed annually to meet global climate goals. Startups like Climeworks, a Swiss company specializing in carbon capture and storage technology, demonstrate the transformative potential of such investments. Climeworks raised over $650 million in 2022, enabling it to scale its direct air capture systems, which remove CO2 directly from the atmosphere (Climeworks, 2022). Increasing investment in such innovative solutions can fast-track the development of technologies that combat climate change while creating new economic opportunities.

Public-Private Partnerships

Public-private partnerships (PPPs) are critical in fostering climate entrepreneurship by combining the resources, expertise, and influence of both sectors. For example, Denmark’s Energy Transition Partnership has successfully collaborated with private wind energy companies like Ørsted to make Denmark a leader in renewable energy. Today, wind energy accounts for 47% of Denmark’s electricity consumption, setting a global benchmark for sustainable energy systems (IEA, 2021). Similarly, in India, the government’s partnership with ReNew Power has accelerated the installation of renewable energy infrastructure, contributing to the country’s ambitious goal of achieving 450 GW of renewable energy capacity by 2030 (ReNew Power, 2022). By creating synergies between public resources and private innovation, PPPs can overcome barriers like funding gaps and regulatory hurdles, thereby amplifying the impact of climate entrepreneurship.

Global Knowledge Sharing

The fight against climate change is a global challenge that demands collective action and the sharing of best practices. Initiatives like the United Nations Climate Technology Centre and Network (CTCN) facilitate knowledge exchange between countries, providing entrepreneurs with access to cutting-edge technologies and expertise. For instance, Kenya’s adoption of solar-powered irrigation systems was inspired by similar systems in Israel, a leader in water conservation technology. This innovation has boosted agricultural productivity in Kenya while reducing water waste in a region plagued by droughts (UNEP, 2021). Knowledge-sharing platforms can also empower entrepreneurs in developing countries by bridging the technology gap and fostering cross-border collaborations. By leveraging global networks, climate entrepreneurship can scale its impact and accelerate the transition to a sustainable future.

REFERENCES

1. (2022). Clean Energy Investment Trends.
2. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
3. Becker, G. S. (1964). Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education.
4. Bullard, R. D. (1990). Dumping in Dixie: Race, Class, and Environmental Quality.
5. Creswell, J. W., & Plano Clark, V. L. (2017). Designing and Conducting Mixed Methods Research (3rd ed.). Sage Publications.
6. Curry, J. (1984). Sample Size Rule of Thumb; Populations and Sampling. North Texas State University.
7. (2022). Carbon Capture Impact Report.
8. Ellen MacArthur Foundation. (2020). Circular Economy Reports.
9. (2021). Global Forest Resources Assessment 2020.
10. Freeman, R. E. (1984). Strategic Management: A Stakeholder Approach.
11. (2021). Climate Change 2021: The Physical Science Basis.
12. (2021). Fossil Fuel Subsidies: Global Update.
13. International Renewable Energy Agency (IRENA). (2021). Renewable Energy and Jobs Annual Review.
14. Jambeck, J. R., et al. (2015). Plastic waste inputs from land into the ocean. Science, 347(6223), 768-771.
15. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk.
16. M-KOPA. (2021). Annual Impact Report.
17. North, D. C. (1990). Institutions, Institutional Change, and Economic Performance.
18. Orb Media. (2017). Invisibles: The Plastic Inside Us.
19. ReNew Power. (2022). Annual Sustainability Report.
20. Rogers, E. M. (1962). Diffusion of Innovations.
21. Swiss Re. (2022). Natural Catastrophes and Climate Report.
22. Schumpeter, J. A. (1934). The Theory of Economic Development.
23. (2022). Tesla Impact Report.
24. (2019). Global Biodiversity Outlook 5.
25. United Nations. (2022). Emissions Gap Report.
26. (2019). Global Biodiversity Outlook 5.
27. United Nations. (2022). World Population Data Report.
28. (2021). Living Planet Report.
29. World Bank. (2022). World Development Indicators.