Effects of Industrialization on Business and Economic Climate in Nigeria: Evidence from Sectoral Analysis

Abstract

This study assessed the dynamics of industrialization in Nigeria from 1999 to 2022 and the ripple effect on environmental quality, business and the economic landscape. This study anchors on the United Nations’ Sustainable Development Goals to assess the interactive effect of industrialization on sectoral value addition while also considering its implications for clean energy, economic growth, innovation and climate change in Nigeria. The autoregressive distributed lag model was employed for its capability to incorporate variables of diverse orders of integration and assess the short- and long-run dynamics of industrialization and its impact on various facets of Nigeria’s economy, business sector and environmental quality. The data set was sourced and collated from the World Bank Development Index. The findings reveal a positive and significant relationship between industrialization and economic growth. The positive nexus is propelled by private sector credit specifically geared towards business development and the pivotal role of net foreign direct investment, for economic expansion alongside industrial activities. Exchange rate volatility limits access to reliable energy sources and impedes sectoral additions. The effect of industrialization on environmental quality stems from heavy reliance on fossil fuels. This study advocates for strategic investments in sustainable and renewable energy sources, adoption of climate-smart agricultural practices and implementation of stringent environmental quality guidelines. To address the dual challenge of fostering economic growth through industrialization, ensuring environmental sustainability and offering valuable insights for policymakers and stakeholders in Nigeria’s development trajectory.

Keywords

Industrialization manufacturing agriculture services sector

Highlights

This study evaluates industrialization’s effect on Nigeria’s economy, business sector and environmental quality, aligning with United Nations’ Sustainable Development Goals.

Utilizing the autoregressive distributed lag model for short- and long-run analysis of collated data from the World Bank Development Index.

Findings reveal a positive nexus between industrialization and economic growth, driven by private sector credit and net foreign direct investment.

Exchange rate volatility hampers energy access and fossil fuel reliance impacts environmental quality, indicating the need for sustainable energy sources.

The study advocates for sustainable energy investments, climate-smart agriculture and stringent environmental guidelines.

1. Introduction

In the twenty-first century, Nigeria’s inclusive growth and development objectives revolved around structural economic transformation, CO₂ emission reduction and industrialization. Globally, the uniqueness of the nexus between human survival and economic activity forms the foundation of erraticism regarding environmental quality. This can be attributed to the 1750 Industrial Revolution, which paved the way for climate change. This is evident in the geometric increase in greenhouse gas (GHG) reaching 409.8 ppm (parts per million) in 2019 and 417.1 ppm in 2022, which is higher than at any point in the past 800,000 years. Similarly, the CO₂ emission concentration fluctuated between 180 and 280 ppm before the industrial revolution, increasing to approximately 417.06 ppm in 2022. As one of Africa’s largest economies, with a gross domestic product (GDP) of $477 billion and a total score of 1.97 for startups in Africa with 135-point ease of doing business index as of 2023, Nigeria acknowledges the profound influence of structural changes through industrial sector reforms to stimulate productive eco-friendly economic and business growth (Abner et al., 2023; Su & Yao, 2017; Udo et al., 2021). The structural transformation is primarily geared towards reducing reliance on fossil energy consumption, while simultaneously improving environmental quality through investment in green energy and building resilience to both internal and external economic shocks. These objectives closely align with the United Nations Sustainable Development Goals (SDGs), particularly for clean energy (SDG 7), economic growth (SDG 8), industry and innovation (SDG 9) and climate action (SDG 13).

Industrialization offers diverse economies of scale in various sectors (Kaldor, 1967). The manufacturing sector habitually enjoys substantial economies of scale, as fixed costs (machinery and infrastructure) can be spread across a larger production volume to reduce operational costs, while significantly increasing output. In contrast, economies of scale in the agricultural sector are diminished by factors such as the slow adoption of climate-smart agricultural practices, fragmented landholding, lack of basic infrastructure, access to sectorial credit, environmental considerations, market access-related challenges, inconsistency and unfavourable government policies in comparison to the manufacturing sector. In the service sector, economies of scale are less pronounced, primarily due to the labour-intensive nature of the industry, characterized by consumers’ desire for personalization and customization of services, and labour efficiency limitations.

According to Cornwall (1977) and Park and Chan (1989), the dynamics and spillover influence of industrialization are more robust in the manufacturing sector than in other sectors within the economy. Smith and Ricardian’s classical growth theories promote industrialization by leveraging the principles of comparative advantage, international trade and capital accumulation. Concurrently, Lewis’s structural transformation theory propounded that the crux of economic and business development on resources shifts from the low-productivity sectors of agriculture to the high-productivity sectors of manufacturing and services. The theory underscores that, in emerging economies such as Nigeria, a surplus labour force within the agricultural sector can be absorbed by the industrial sector to enhance productivity and upsurge earnings. Complementing these theories, Becker’s human capital theory acknowledges the pivotal role of education and skill development in driving industrialization, by urging investment in human capital through educational training, innovation and technological progress. Globalization and environmental quality significantly contribute to the divergent influence of these theories on policy decisions in Nigeria.

In developed economies, which are characterized by heightened environmental awareness and relatively stable economic conditions, industrialization significantly enhances business and economic development. In contrast, the declining influence of industrialization on Nigeria’s economic and business structure is a noteworthy trend due to policy inconsistencies, import dependency, infrastructure and energy challenges (Udo et al., 2021, 2023). Services and agricultural sector-led growth are propelled by technological advancements and a surge in consumer service demand driven by urbanization, rising income and evolving consumer preferences. The influence of the less capital-intensive nature of the service sector and its ability to absorb the rapidly growing youth population with diverse skills and educational backgrounds is evident in the service sector’s 52% contribution to GDP and 7% growth rate in the third quarter of 2022, whereas the agricultural and industrial sectors contributed 30% and 18% to GDP, with growth rates of 1.3% and 8%, respectively.

In Sub-Saharan Africa (SSA), Bhorat et al. (2017) observed a stagnated five-year manufacturing sector contribution to the GDP. Prior empirical findings of Udo et al. (2021, 2023), particularly in Nigeria, linked the manufacturing sector’s contribution to the post-independence industrialization policy framework, which focused on import substitution and infant industry protection. Similarly, Udoh et al. (2019) ascribed the declining influence of Nigeria’s manufacturing sector to insecurity issues such as the activities of bandits and militant Islamist Boko Haram terrorist groups, Niger Delta militants, unknown gunmen and other crimes such as kidnapping and farmer/herdsmen (Udoh et al., 2019). Trade liberalization and globalization policies expose the sector to unnecessary global market competition and several other global economic shocks, such as COVID-19 and Russia’s invasion of Ukraine (Udoh et al., 2023), also contributing to the manufacturing sector’s contribution to GDP in Nigeria. This is evident in the decline of net foreign investment inflow to the manufacturing sector by 35% from $392.5 million in the fourth quarter of 2022 to $256 million in the first quarter of 2023. In the first quarter of 2022, it declined by 28%, from $1.5 billion to $1.1 billion in the first quarter of 2023 in the manufacturing sector. Despite the declining contribution of the manufacturing sector to economic growth due to the harsh business and economic climate, community-based industrialization, according to the United Nations Conference on Trade and Development (UNCTAD), the Economic Commission for Africa (ECA), the African Union (2014) and others, has the potential to swiftly rejig Nigeria and Africa’s economic and business growth. The sustainability and transformative potential of community-based industrialization anchored in Nigeria’s competitive advantage prowess and favourable policy framework (Szirmai et al., 2013; Udo et al., 2024). The strategic position and active role of Nigeria in the continent and regional blocs collectively indicate a massive market base for the industrial sector development. Similar to other emerging African economies, Nigeria’s service sector shows a geometric increase in its contribution to the GDP, value addition and employment opportunities (Figure 1).

Despite scarce development and investment in green energy sources, fossil energy sources have sufficiently filled the energy demand–supply gap by providing 80% of global energy needs and contributing approximately 66.667% to total GHG CO₂ emissions (Abner et al., 2023). Theoretically, this study supports the environmental Kuznets curve (EKC), which explains the economic growth-environmental degradation nexus; the pollution haven hypothesis (PHH) explains the globalization-environmental degradation nexus; and the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) explains the population growth-environmental degradation nexus. Kahuthu’s (2006) findings confirmed an inverted U-shaped nexus between income growth and carbon emissions. According to the EKC hypothesis, an increase in economic growth decreases environmental quality through GHG emissions at the initial stage of growth. As the economy evolves with increasing income level, environmental quality is sustained with income in the long term. This study provides insight into the industrialization effect on Nigeria’s business and economic growth and environmental quality while advancing the frontiers of the study of Lugina et al. (2022) in Tanzania, which focused exclusively on industrialization, neglecting environmental quality effects and sectoral nuances.

Previous studies documenting this nexus have focused on the short-term impacts and macroeconomic effects of industrialization while ignoring specific implications for the business climate. Longitudinal studies have focused on both the long and short impacts of industrialization in developed economies, neglecting emerging economies, such as Nigeria, to consider vulnerable to climate change due to limited adaptive capacity to climate change impacts, institutional vulnerabilities, economic activities such as mining, logging and agriculture, reliance on traditional biomass and geographic and climatic characteristics. Previous studies documenting this nexus relied on panel studies to report diverse results in developed and emerging countries. These studies have ignored country-specific heterogeneous factors, and the diversity of their results can be traced to the scope, techniques of analysis, variables, types of data and other factors.

Kapoor (2015) observed a negative nexus in India, whereas a positive nexus was observed in middle-income economies (Su & Yao, 2017; Udo et al., 2024) in MINT countries; 37 African countries (Moyo & Jeke, 2019), 28 African countries and 37 African countries (Opoku & Yan, 2018); The findings of these studies revealed that industrialization exacerbates environmental pollution, thus supporting PHH. Paramati et al. (2021) argue that industrialization through green technology enhances environmental quality, thus supporting the pollution halo hypothesis (PH).

Based on this lacuna, this study makes significant contributions by assessing the effects of industrialization on Nigeria’s environmental quality and economic and business landscapes. Climate change is a quarrelsome issue, particularly in Africa, where seven of the ten countries, including Nigeria, are vulnerable to climate change due to geographical, economic, social and institutional vulnerabilities. This study takes a departure from previous studies focusing on either economic or environmental impacts in isolation. Using a longitudinal analysis of the autoregressive distributed lag (ARDL) bounce model, this study captures both short- and long-run effects of industrialization, providing a more nuanced understanding of its impacts on businesses and environmental quality. This methodological approach enhances the precision and reliability of the results, filling a gap ignored by previous studies focusing on the short-term impacts.

By considering regional variations in the effects of industrialization, this study provides a more localized understanding of targeted policy intervention. Ten of the seven countries that are greatly affected by climate change are African and Nigeria is inclusive. Lax environmental policies and low compliance costs allow carbon-intensive multinational and domestic firms to exploit to deplete Nigeria’s environmental quality. This study distinguishes between the effects of industrialization across diverse sectors (manufacturing, agriculture and services), highlighting the specific challenges and opportunities within each. This sectoral analysis provides a deeper insight into the diverse impacts of industrialization on Nigeria’s economy, departing from previous studies relying on cross-sectional studies, thereby offering a more focused and detailed analysis. The study objectives are placed within the broader context of inclusive growth, structural economic transformation and CO₂ emission reduction. Aligning with the United Nations’ SDGs to underscore the importance of addressing global sustainability challenges. Theoretically, this study underpins the link between industrialization, economic growth and environmental quality and contextually provides insight into Nigeria’s development trajectory within its broader socio-economic landscape. Thus, providing a comprehensive and multifaceted contribution to the nexus between industrialization, economic growth and environmental quality in Nigeria. This study also provides valuable insights for policymakers, academics and stakeholders interested in sustainable development and economic transformation.

The study is systematized as follows: First, we present a literature and theoretical review of Nicholas Kaldorian theory, Big Push theory, ecological modernization theory and critiques of ecological modernization theory. The next section presents an empirical review, followed by the methodology, model explanation, variables and data collection. The following section discusses the findings and conclusion, theoretical contribution, practical implementation and recommendations for future research.

2. Literature Review

Theoretically, this study underpins the link between industrialization, economic growth and environmental quality and contextually provides insight into Nigeria’s development trajectory and the need for strategic interventions. A summary of the literature and theoretical reviews is presented in Tables 1 and 2, respectively.

Table 1.

Summary of Literature Review.

Period	Economic Landscape	Key Events and Policies	Major Theories	Main Issues and Challenges
Pre-1960	Agrarian economy with a focus on food processing, textiles and small-scale manufacturing	Primarily agrarian structure	N/A	Limited industrial diversification and reliance on agrarian activities
1960s–1970s	Import substitution industrialization (ISI) era aims to reduce reliance on imports and develop domestic industries	ISI plan with a focus on domestic industry growth	Inefficiency in resource use during the ISI era	ISI era was unable to achieve objectives, exposing the economy to external shocks
1970s–1980s	Oil boom from the discovery of oil in commercial quantities	Oil boom significantly boosts revenue	N/A	Neglect of non-oil sectors, external shocks from oil price fluctuations
1980s–1990s	The structural adjustment programme (SAP) era focused on diversification, liberalization, currency devaluation and trade reforms	SAP era under international financial institutions’ guidance	Free trade hampers diversification and increases inequality	Decreased social benefits, unemployment, and economic inequalities
2000–present	Democracy and economic reform era emphasizing economic reforms, policy changes, private sector participation and green energy sources	Emphasis on economic reforms, policy changes and green energy investments	Private sector participation, foreign investment attraction	Striving for industrial revival, improving ease of doing business and addressing environmental concerns

Table 2.

Summary of Theoretical Reviews.

Theories	Key Points
Nicholas Kaldorian Theory of 1967	Recognizes the pivotal role of the manufacturing sector in achieving inclusive growth in developed economies. The manufacturing output growth and economic growth laws support a positive nexus
Kaldorian’s theoretical characteristics explains the manufacturing output growth and economic growth nexus and its implications	The manufacturing output growth and economic growth laws support a positive manufacturing output growth and economic growth nexus using the regression model [gGDP = a₁ + b₁ + gm]Kaldor’s equation indicates that services habitually depend on finished goods, and a significant portion of exports come from finished goods
	Verdoorn’s output and productivity growth law related to Kaldor’s theory posits that output growth fast-tracks growth efficiency through industrial sector labour efficiency. An increase in industrial sector efficiency decreases labour costs and accordingly lowers price levels
	The two-sector model explains the nexus of the industrial and agricultural sectors through the positive non-industrial and industrial sector output growth nexus. The model acknowledges the existence of unemployment in the initial stages of the service and agricultural sector growth. As resources shift from non-industrial to industrial sectors, the average productivity of residual sectors increases because of diminishing returns to scale
Big push theory	Big push theory underscores the importance of synchronized investments in diverse economic sectors for the development of domestic market of non-industrialized countries like Nigeria. Rosenstein-Rodan’s (1943) theory addresses market constraints, minimum investment requirements and economies of scale. Market constraints are a key impediment to industrialization in non-industrialized countries, and can be attributed to low per capita income, inequality income spread and others. Rosenstein-Rodan (1943) posits that to check market constraints in non-industrialized economies, a minimum level of economic-financial resources must be available, but this is not a fitting pre-requisite to achieving inclusive growth. To benefit from economies of scale and overcome market constraints, Rosenstein–Rodan (1943) acknowledged the significance of economic size through the pooling of resources and investments from other economic sectors. This implies that the growth of one sector creates a demand for products from other sectors, which leads to a positive multiplier effect on both sectors and individuals to profit from production
Ecological modernization theory (EMT)	This theory provides insights into how industrialized economies address environmental challenges. The theory posits that modernization and environmental sustainability are not mutually exclusive, but can be reconciled through technological innovation, regulatory reforms and changes in societal values. The environmental quality-industrialization nexus is non-linear; as such, throughout the primary phases of modernization, environmental quality declines as a result of resource exploitation, primitive technology, urbanization, lack of environmental awareness and regulations when industries are not fully industrialized (Rehman & Sohag, 2022). In the long run, due to high environmental awareness, compliance costs, regulations and changes in societal values, industries can adopt eco-friendly technologies to reduce CO₂ emissions during production
Critiques of EMT	EMTs excessively rely on technological innovation to solve environmental problems. Technology alone is not sufficient to address the complex ecological challenges. The EMT fails to address disparities in environmental impacts and benefits, as it may disproportionately affect marginalized communities, leading to environmental justice issues. This theory underestimates the influence of influential economic agents who may resist environmental reforms because industries with vested interests in maintaining the status quo may thwart regulatory changes

2.1 Empirical Review

Studies examining the industrialization, economic and business landscape nexus in Nigeria have largely focused on the manufacturing sector. Studies examining the industrialization and environmental quality nexus have focused on developed economies with high environmental quality awareness, regulations and compliance costs, negating emerging economies like Nigeria suffering from climate change issues due to limited adaptive capacity to climate change impacts; institutional vulnerabilities; economic activities such as mining, logging and agriculture; reliance on traditional biomass; and geographic and climatic characteristics. The contribution of the manufacturing sector to economic growth in Nigeria has been questioned in recent years because of the declining and increasing influence of the service and agricultural sectors on the economic and business climate, which presents a new frontier assessed in this study. Previous studies documenting this nexus have relied on panel studies to report diverse results in developed and emerging countries. These studies have ignored country-specific heterogeneous factors, and the diversity of their results can be traced to the scope, techniques of analysis, variables, types of data and other factors. In addition, the ripple effect of industrialization on environmental quality has been neglected in previous studies, this study presents another frontier, climate change is a quarrelsome issue as it endangers both sustainable and human growth. Ten of the seven countries greatly affected by climate change are African countries, and Nigeria suffers from climate change issues such as floods, droughts, windstorms, loss of biodiversity and drying of water bodies. The findings of Kapoor (2015) for India revealed a negative nexus, whereas a positive nexus was observed in middle-income economies (Su & Yao, 2017): 37 African countries (Moyo & Jeke, 2019), 28 African countries and 37 African countries (Opoku & Yan, 2018). The findings of these studies revealed that industrialization exacerbates environmental pollution, thus supporting PHH and the findings of Udo et al. (2024). Paramati et al. (2021) argue that industrialization through green technology enhances environmental quality, thus supporting the PH and the findings of Udo et al. (2024). Table 3 presents a summary of the empirical review.

Table 3.

Empirical Reviews.

Author	Scope	Techniques of Analysis	Results
Industrialization and economic growth nexus
Kapoor (2015)	India	Regression	Negative
Su and Yao (2017)	Middle-income countries	Panel regression	Positive
Opoku and Yan (2018)	37 African countries	Panel regression	Positive
Udo et al. (2023)	Nigeria	ARDL	Positive
Industrialization and environmental quality nexus
Ahmed et al. (2022)	Asia-Pacific region	Panel regression	Positive
Jermsittiparsert (2021)	ASEAN countries	ARDL
Mahmood et al. (2020)	Saudi Arabia	Non-linear ARDL
Nasir et al. (2021)	Australia	Panel ARDL STIRPATS model
Nasrollahi et al. (2022)	MENA and OECD countries	Panel ARDL STIRPATS model	Negative
Technology and environmental quality
Khan et al. (2022)	Morocco	ARDL approach	Technological innovation reduces CO₂ emissions in the long run
Anwar et al. (2021)	G7 countries 1996–2018
Jahanger et al. (2022)	73 developing economies 1990–2016

Note: ARDL, autoregressive distributed lag; STIRPAT, Stochastic Impacts by Regression on Population, Affluence, and Technology.

3. Materials and Methods

3.1 Nature and Data Sources

The secondary annualized time-series data were collated from the World Bank (2022) indicators covering the era of democracy and economic reform (1999–2022). We adopt the ARDL model to provide a dynamic perspective on the long-run nexus between industrialization and its impact on the business and economic climate, sectoral value addition and environmental quality. Three assessment models were used in this study.

H₁: The industrialization effect on the business and economic landscape.

H₂: Industrialization effect on sectoral (manufacturing, agricultural and service) value addition.

H₃: The industrialization effect on environmental quality.

Distinct from static models, the ARDL captures the evolving nature of the relationship, which allows for a more nuanced understanding of how industrialization influences the variables of interest over time, incorporates lagged values of the dependent variable, and provides a mechanism to capture any delayed effects or feedback loops in the nexus between industrialization and the specified outcomes.

3.1.1 Industrialization Effect on the Business and Economic Landscape

\begin{array}{l} {GDP}_{t} = β_{0} + \sum_{i = 1}^{p} β_{i} {GDP}_{t - i} + \sum_{i = 0}^{k 1} δ_{i} {MAN}_{t - i} + \sum_{i = 0}^{k 2} λ_{i} {AGR}_{t - i} + \sum_{i = 0}^{k 3} γ_{i} {SERV}_{t - i} + \sum_{i = 0}^{k 4} ϕ_{i} {AELE}_{t - i} + \sum_{i = 0}^{k 5} π_{i} {EXCH}_{- i} + \sum_{i = 0}^{k 6} θ_{i} {PCS}_{t - i} + ε_{t} \end{array}

(1)

Δ I n {GDP}_{t} = β_{0} + \sum_{i = 1}^{p} β i Δ I n {GDP}_{t - i} + \sum_{i = 0}^{k 1} δ_{i} Δ I n {MAN}_{- i} + \sum_{i = 0}^{k 2} λ_{i} Δ I n {AGR}_{t - i} + \sum_{i = 0}^{k 3} γ_{i} Δ I n {SERV}_{t - i} + \sum_{i = 0}^{k 4} ϕ_{i} Δ I n {AELE}_{t - i} + \sum_{i = 0}^{k 5} π_{i} Δ I n {EXCH}_{- i} + \sum_{i = 0}^{k 6} θ_{i} Δ I n {PCS}_{t - i} + λ_{1} I n R {GDP}_{t - 1} + λ_{2} I n {MAN}_{t - 1} + λ_{3} I n {AGR}_{t - 1} + λ_{4} I n {SERV}_{t - 1} + λ_{5} I n {AELE}_{t - 1} + λ_{6} I n {EXCH}_{t - 1} + λ_{7} I n {PCS}_{t -} + ε_{t}

(2)

3.1.2 Industrialization Effect on Sectoral Value Addition

Manufacturing sector:

{MAN}_{t} = β_{0} + \sum_{i = 1}^{p} β_{i} {MAN}_{t - i} + \sum_{i = 0}^{k 1} δ_{i} {FDI}_{t - i} + \sum_{i = 0}^{k 2} λ_{i} {EXCH}_{t - i} + \sum_{i = 0}^{k 3} γ_{i} {PCS}_{- i} + \sum_{i = 0}^{k 4} ϕ_{i} {LFO}_{t - i} + \sum_{i = 0}^{k 5} ϕ_{i} {AELE}_{t - i} + ε_{t}

(3)

\begin{array}{l} Δ I n {MAN}_{t} = β_{0} + Σ_{i = 1}^{p} β_{i} Δ I n {MAN}_{t - i} + Σ_{i = 0}^{k 1} δ_{i} Δ I n {FDI}_{- i} + Σ_{i = 0}^{k 2} λ_{i} Δ I n {EXCH}_{t - i} \\ Σ_{i = 0}^{k 3} γ_{i} Δ n {PCS}_{t - i} + Σ_{i = 0}^{k 4} ϕ_{i} Δ I n {ALFO}_{t - i} + λ_{1} I n {MAN}_{t - 1} + λ_{2} 2 I n {FDI}_{t - 1} \\ λ_{3} I n {EXCH}_{1} + λ_{4} I n {PCS}_{t - 1} + λ_{5} I n {LFO}_{t - 1} + λ_{6} I n {AELE}_{t - 1} + ε_{t} \end{array}

(4)

Agricultural sector:

{AGR}_{t} = β_{0} + \sum_{i = 1}^{p} β_{i} {AGR}_{t - i} + \sum_{i = 0}^{k 1} δ_{i} {FDI}_{t - i} + \sum_{i = 0}^{k 2} λ_{i} {EXCH}_{t - i} + \sum_{i = 0}^{k 3} γ_{i} {PCS}_{- i} + \sum_{i = 0}^{k 4} ϕ_{i} {LFO}_{t - i} + \sum_{i = 0}^{k 5} ϕ_{i} {AELE}_{t - i} + ε_{t}

(5)

Δ I n A G R_{t} = β_{0} + \sum_{i = 1}^{p} β_{i} Δ I n {AGR}_{t - i} + \sum_{i = 0}^{k 1} δ_{i} Δ I n {FDI}_{- i} + \sum_{i = 0}^{k 2} λ_{i} Δ I n {EXCH}_{t - i} + \sum_{i = 0}^{k 3} γ_{i} Δ I n {PCS}_{t - i} + \sum_{i = 0}^{k 4} ϕ_{i} Δ I n {ALFO}_{t - i} + + λ_{1} I n {AGRN}_{t - i} + λ_{2} I n {FDI}_{t - 1} + λ_{3} I n {EXCH}_{1} + λ_{4} I n {PCS}_{t - 1} + λ_{5} I n {LFO}_{t - 1} + λ_{6} I n {AELE}_{t - 1} + ε_{t}

(6)

Service sector:

{SERV}_{t} = β_{0} + \sum_{i = 1}^{p} β_{i} {SERV}_{t - i} + \sum_{i = 0}^{k 1} δ_{i} {FDI}_{t - i} + \sum_{i = 0}^{k 2} λ_{i} {EXCH}_{t - i} + \sum_{i = 0}^{k 3} γ_{i} {PCS}_{- i} + \sum_{i = 0}^{k 4} ϕ_{i} {LFO}_{t - i} + \sum_{i = 0}^{k 5} ϕ_{i} {AELE}_{t - i} + ε_{t}

(7)

Δ I n {SERV}_{t} = β_{0} + \sum_{i = 1}^{p} β_{i} Δ I n {SERV}_{t - i} + \sum_{i = 0}^{k 1} δ_{i} Δ I n {FDI}_{- i} + \sum_{i = 0}^{k 2} λ_{i} Δ I n {EXCH}_{t - i} + \sum_{i = 0}^{k 3} γ_{i} Δ I n {PCS}_{t - i} + \sum_{i = 0}^{k 4} ϕ_{i} Δ I n {ALFO}_{t - i} + + λ_{1} I n {SERV}_{t - i} + λ_{2} I n {FDI}_{t - 1} + λ_{3} I n {EXCH}_{1} + λ_{4} I n {PCS}_{t - 1} + λ_{5} I n {LFO}_{t - 1} + λ_{6} I n {AELE}_{t - 1} + ε_{t}

(8)

3.1.3 Industrialization on Environmental Quality (CO₂ emission)

\begin{array}{l} CO 2_{t} = β_{0} + \sum_{i = 1}^{p} β_{i} CO 2_{t - i} + \sum_{i = 0}^{k 1} δ_{i} {MAN}_{t - i} + \sum_{i = 0}^{k 2} λ_{i} {AGR}_{t - i} + \sum_{i = 0}^{k 3} γ_{i} {SERV}_{t - i} + \sum_{i = 0}^{k 4} ϕ_{i} {AELE}_{t - i} + \sum_{i = 0}^{k 5} π_{i} {EXCH}_{- i} + \sum_{i = 0}^{k 6} θ_{i} {PCS}_{t - i} + \sum_{i = 0}^{k 7} θ_{i} {RGDP}_{t - i} + ε_{t} \end{array}

(9)

Δ I n {C O 2}_{t} = β_{0} + \sum_{i = 1}^{p} β_{i} Δ I n {C O 2}_{t - 1} + \sum_{i = 0}^{k 1} δ_{i} Δ I n {M A N}_{- i} + \sum_{i = 0}^{k 2} λ_{i} Δ I n {A G R}_{t - i} + \sum_{i = 0}^{k 3} γ_{i} Δ I n {S E R V}_{t - i} + \sum_{i = 0}^{k 4} ϕ_{i} Δ I n {A E L E}_{t - i} + \sum_{i = 0}^{k 5} π_{i} Δ I n {E X C H}_{- i} + \sum_{i = 0}^{k 6} θ_{i} Δ I n {P C S}_{t - i} + \sum_{i = 0}^{k 7} θ_{i} Δ I n {R D P}_{t - i} + λ_{1} I n {C O 2}_{t - 1} + λ_{2} I n {M A N}_{t - 1} + λ_{3} I n {A G R}_{t - 1} + λ_{4} I n {S E R V}_{t - 1} + λ_{5} I n {A E L E}_{t - 1} + λ_{6} I n {E X C H}_{t - 1} + λ_{7} I n {P C S}_{t -} + λ_{8} I n {R D P}_{t -} + ε_{t}

(10)

The ARDL bound test is expressed by Equation (3) as follows:

where ∇ = difference operator and ln = the natural log of the variables.

The F-statistic value of the bound test was estimated to assess the presence of a long-run nexus among the variables, as prescribed by Pesaran et al. (2001). The values of the estimated F-statistics were compared to the upper and lower critical values.

Bound Test Decision Rule: If the calculated F-statistics are greater than the upper critical value, reject the null hypothesis of no co-integration; if the value of the F-statistics is less than the lower critical value, accept the null; inconclusive scenario arises from where the value of the F-statistics falls between the upper and lower critical values. From Equations (3, 4, 6, 8, 10), the short-run dynamics are denoted by i for i = 1, 2, 3, 4, 5, . . ., 11, and the long-run dynamics are captured by βi, γi, δi, ρi, τi, υi, θi, ωi, ϕi, χi, and σi for i = 1, 2, 3, 4, ..., p.

3.1.4 Error Correction Model (ECM) Equations

The ECM equations (3; 8; 9; 12; 14) are expressed in (11; 12; 13; 14; 15):

Δ I n R {GDP}_{t} = β_{0} + \sum_{i = 1}^{p} β_{i} Δ I n R {GDP}_{t - i} + \sum_{i = 0}^{p} δ_{i} Δ I n {MAN}_{t - i} + \sum_{i = 0}^{p} λ_{i} Δ I n {AGR}_{t - i} + \sum_{i = 0}^{p} γ_{i} " I n {SERV}_{t - i} + \sum_{i = 0}^{p} ϕ_{i} Δ I n {AELE}_{t - i} + \sum_{i = 0}^{p} π_{i} Δ I n {EXCH}_{t - i} + \sum_{i = 0}^{p} θ_{i} Δ I n {PCS}_{t - i} + \infty {ECT}_{t - 1} + ε_{t}

(11)

Δ I n {MAN}_{t} = β_{0} + \sum_{i = 1}^{p} β_{i} Δ I n {MAN}_{t - i} + \sum_{i = 0}^{p} δ_{i} Δ I n {FDI}_{t - i} + \sum_{i = 0}^{p} λ_{i} Δ I n {EXCH}_{t - i} + \sum_{i = 0}^{p} γ_{i} Δ I n {PCS}_{t - i} + \sum_{i = 0}^{p} ϕ_{i} Δ I n {AELE}_{t - i} + \sum_{i = 0}^{p} ϕ_{i} Δ I n {LFOM}_{t - i} \infty {ECT}_{t - 1} + ε_{t}

(12)

Δ I n {AGR}_{t} = β_{0} + Σ_{i = 1}^{p} β Δ I n {AGR}_{t - i} + Σ_{i = 0}^{p} δ Δ I n {FDI}_{t - i} + Σ_{i = 0}^{p} λ_{i} Δ I n {EXCH}_{t - i} + Σ_{i = 0}^{p} γ_{i} Δ I n {PCS}_{t - i} + Σ_{i = 0}^{p} ϕ Δ I n {AELE}_{t - i} + Σ_{i = 0}^{p} ϕ_{i} Δ I n {LFOA}_{t - i} \infty {ECT}_{t - 1} + ε_{t}

(13)

Δ I n {SERV}_{t} = β_{0} + \sum_{i = 1}^{p} β_{i} Δ I n {SERV}_{t - i} + \sum_{i = 0}^{p} δ_{i} Δ I n {FDI}_{t - i} + \sum_{i = 0}^{p} λ_{i} Δ I n {EXCH}_{t - i} + \sum_{i = 0}^{p} γ_{i} Δ I n {PCS}_{t - i} + \sum_{i = 0}^{p} ϕ_{i} Δ I n {AELE}_{t - i} + \sum_{i = 0}^{p} ϕ_{i} Δ I n {LFOS}_{t - i} + \infty {ECT}_{t - 1} + ε_{t}

(14)

Δ I n C O 2_{t} = β_{0} + \sum_{i = 1}^{p} β_{i} Δ I n C O 2_{t - i} + \sum_{i = 0}^{p} δ_{i} Δ I n {MAN}_{t - i} + \sum_{i = 0}^{p} λ_{i} Δ I n {AGR}_{t - i} + \sum_{i = 0}^{p} γ_{i} Δ I n {SERV}_{t - i} + \sum_{i = 0}^{p} ϕ_{i} Δ I n {AELE}_{t - i} + \sum_{i = 0}^{p} π_{i} Δ I n {EXCH}_{t - i} + \sum_{i = 0}^{p} θ_{i} Δ I n {PCS}_{t - i} + \infty {ECT}_{t - 1} + ε_{t}

(15)

ECT = error correction term that captures the long-run nexus between variables. The coefficient of (∞) indicates the speed of convergence to the long-run equilibrium from the short-run divergence due to shocks in the system. ∞ is expected to be negative and significant after an external shock. The diagnostic test results include autoregressive conditional heteroscedasticity (ARCH), Breusch–Godfrey (BG) test for serial correlation and Jarque–Bera (JB) test for normality.

where GDP = GDP per capita (constant 2015 $); MAN = manufacturing, value-added (% of GDP); AGR = agriculture value-added (% of GDP); SERV = services, value-added (% of GDP); EXCH = exchange rate; PCS = private sector credit; AELE = access to electricity; FDI = foreign direct investment inflows; LFO = sectoral labour force (% of total employment (agriculture (LOFA), manufacturing (LOFM), and services (LOFS)); and CO₂ = carbon dioxide emission (environmental quality).

3.2 Variable Descriptions

Table 4 presents the variables and data descriptions.

Table 4.

Variable Descriptions and Units.

Variables	Proxied	Data Source
Carbon emission (CO₂)	Environmental pollution	World Bank Development Indicators (WDI) World Bank (2023)
GDP per capita (constant $ 2015)	Economic growth
Industrialization	Manufacturing, value added (% of GDP)
	Agriculture, value added (% of GDP)
	Services, value added (% of GDP)
Factors influencing industrialization (control variables)	Exchange rate
	Private sector credit
	Access to electricity
	Net foreign direct investment inflows
	Sectoral labour force

4. Results and Discussions

The results of the descriptive statistics are presented in Table 5 in non-logarithmic form. The mean and median values of the series were similar. This proximity implies that there was no extreme projection. The positive mean return of the series indicates an increasing propensity effect of industrialization on environmental quality and the economic and business climate in Nigeria. The low standard deviation values compared to the mean values indicate that the variables are not highly volatile around the mean. The kurtosis values of AGR, LOFM and CO₂ are leptokurtic (>3), whereas those of GDP, MAN, SERV, EXCH, LOFA, LFOS, AELE and FDI are platykurtic (<3). The JB test results show that the series is normally distributed at the 0.05% significance level, except for AGR and CO₂.

Table 5.

Descriptive Statistics.

	GDP	MAN	AGR	SERV	LOFA	LFOS	LOFM	EXCH	PSC	FDI	EXCH	AELE
Mean	7.68	10.62	24.30	48.38	41.15	47.49	11.71	5.197	11.65	1.39	203.85	52.05
Median	7.78	9.64	23.89	48.97	40.58	49.27	11.63	5.03	11.15	1.52	153.86	52.50
Std. Dev.	0.19	2.701	3.961	5.324	4.913	4.812	1.013	0.483	3.182	0.80	108.81	5.442
Skewness	–0.96	0.445	1.778	0.258	0.395	–0.501	0.939	0.605	0.920	0.12	0.976	–0.006
Kurtosis	2.65	2.039	6.217	2.617	1.763	1.877	4.414	1.950	3.443	2.14	2.467	1.925
Jarque–Bera	4.03	1.790	23.96	0.431	2.246	2.361	5.758	2.674	3.435	0.83	4.266	1.202
Prob.	0.13	0.408	0.006	0.806	0.325	0.307	0.056	0.262	0.179	0.65	0.118	0.548

The stationarity properties of the series were tested to determine their order of integration using the first-generation unit test model of augmented Dickey–Fuller (ADF) before testing for co-integration. The results presented in Table 6 show that the series is stationary at level I (0) and the first difference I (1) order of integration. The results give credit to our adopted ARDL model to test for co-integration and meet the Gauss–Markov conditions for unbiased estimation.

Table 6.

Augmented Dickey–Fuller (ADF) Unit Root Test Results.

Variables	ADF Stat	Critical Values 5%	Order of Integration
Trend and intercept
GDP	–8.365	–3.6222	I (1)
MAN	–4.692	–3.644	I (1)
AGR	–5.347	–3.622	I (1)
SERV	–5.268	–3.658	I (1)
EXCH	–4.402	–3.632	I (1)
LOFA	–6.364	–4.956	I (0)
LOFM	–4.525	–2.983	I (0)
LFOS	–4.851	–3.269	I (0)
CO2	–3.532	–1.342	I (0)
AELE	–5.866	–3.612	I (0)
PSC	–5.887	–3.754	I (0)
FDI	–7.949	–3.622	I (1)

4.1 Co-integrating Bound Test Results

The long-run co-integrating nexus results are presented in Panel A of Table 7, which shows that the F-statistics result values of <1(1) in the model hypotheses are compared with the critical values at the 5% significance level. These results suggest the presence of a long-term cointegrating nexus between the study variables. On the premise of co-integration, the ECM was employed to test the speed of convergence from short-run divergence to long-run symmetry. The diagnostic test results are presented in Panel B of Table 7, indicating that the BG–Lagrange multiplier (LM) test for autocorrelation was greater than 0.5, implying the absence of autocorrelation. The Breusch–Pagan Godfrey (BPG) test for heteroscedasticity confirms that the model’s residuals are homoscedastic.

Table 7.

Autoregressive Distributed Lag (ARDL) Cointegration Test Results.

Panel A: Long-run Bound Test Estimation						Panel B: Diagnostic Tests (p values of the F-statistics)
			Test Statistics (5%)			BG LM Test	BPG	ARCH Test (1)
Variables		Models	F-stat	I (0)	I (1)
GDP (H₁)	MAN	2,0,1,0,0	6.421	2.270	3.280	0.178	0.385	0.751
	AGR
	SERV
	AELE
	EXCH
	PCS
Sectoral analysis (H₂)
MAN	FDI	3,2,2,2,2	12.532	2.390	3.380	0.531	0.696	0.433
	EXCH
	PCS
	LFOM
	AELE
AGR	FDI	2,0,1,1,0	8.086	2.390	3.380	0.191	0.871	0.326
	EXCH
	PCS
	LFOA
	AELE
SERV	FDI	3,2,2,1,2	9.575	2.390	3.380	0.649	0.263	0.313
	EXCH
	PCS
	LFOS
	AELE
Environmental pollution (H₃)
CO2	MAN	3,1,1,1,0	8.286	2.170	3.321	0.426	0.410	0.982
	AGR
	SERV
	GDP
	AELE
	EXCH
	PCS

Note: Breusch–Godfrey (BG) serial Lagrange multiplier (LM) test; Breusch–Pagan Godfrey (BPG) test; heteroscedasticity (ARCH) test.

4.1.2 Error Correction Model

The ECM results presented in Table 8 show that the CoinEq (–1) values of (–0.949), (–0.303), (–0.875), (–0.894) and (–0.172) are rightly signed ‘negative and significant’, indicating the speed of convergence to the long-run equilibrium from the short-run deviations. The CoinEq (–1) value of (–0.949) shows a 94% annual speed of convergence, indicating that resourceful government policies and favourable global economic conditions can increase the demand for Nigeria’s industrial goods in one year. The CointEq (–1) value of (–0.303) shows a slower pace of convergence to changes in the manufacturing sector’s responsiveness to industrialization. This implies that the manufacturing sector’s contribution to economic growth can converge back to long-run equilibrium in three years and three months from the short-run disequilibrium caused by insecurity, high operational costs, inadequate infrastructural development, lack of access to affordable and efficient energy supplies and technological adoption.

Table 8.

Short Run Error Correction Estimation (ECM) Model.

						Other Parameter Estimate
Model	DP	CointEq (–1)	Coefficient	T-stat	Prob.	R ²	Adjust. R²	DW Stat
H ₁	GDP	–0.949	0.118	–8.0046	0.0000	0.92	0.91	2.20
H ₂	MAN	–0.303	0.030	–10.079	0.000	0.82	0.78	2.10
	AGR	–0.875	0.076	–11.517	0.000	0.90	0.88	2.08
	SERV	–0.894	0.049	–17.933	0.000	0.90	0.88	2.29
H ₃	CO2	–0.172	0.016	–10.655	0.000	0.82	0.78	2.07

Note: Dependent variables (DP).

A CoinEq (–1) value of (–0.875) indicates an 87% annual speed of convergence in the agricultural sector from short-run deviations. This implies that the agricultural sector’s contribution to economic growth converged back to long-run equilibrium in three years and three months from short-run disequilibrium, which was the result of investments in modern agricultural technologies and the introduction of smart agricultural practices to ensure food security. The CoinEq (–1) value of (–0.894) shows an 89% annual speed of convergence to changes in the service sector as a result of changes in consumer preferences and behaviours, adaptability and diversity in the sector. This implies that the services sector’s contribution to economic growth converged back to long-run equilibrium in one year and one month from the short-run disequilibrium.

A CoinEq (–1) value of (–0.172) showed a slower 17% annual speed of convergence to changes in environmental quality. This implies that the contribution of environmental quality to economic growth converged back to long-run equilibrium in eight years and three months from short-run disequilibrium caused by lax enforcement of environmental regulations, compliance costs and a lack of awareness and investment in green energy development. These results supported the EMT hypothesis. The results show that government policies, infrastructural development, technology and regulations jointly influence environmental quality and the economic and business climate in Nigeria. Policymakers must consider these factors in the development of industrialization strategies to achieve the UN SDGs in Nigeria.

4.2 Discussion of the Long-Run Estimate Results

The long-run result estimates for the three tested hypotheses are presented in Table 9.

Table 9.

Autoregressive Distributed Lag (ARDL) Long Run Estimate.

						Other Parameter Estimate
Hypotheses		Coefficient	Std. Error	T-statistics	Prob.	R ²	Adjusted R²	F-stat	DW
GDP (H₁)	MAN	0.804	0.079	10.073	0.000	0.99	0.99	242.738 (0.000)	2.30
	AGR	0.746	0.106	7.012	0.000
	SERV	0.837	0.101	8.209	0.000
	AELE	0.012	0.088	0.137	0.989
	EXCH	0.749	0.214	3.495	0.006
	PCS	–0.013	0.010	–1.326	0.217
	C	5.529	0.944	5.851	0.002
MAN (H₂)	FDI	0.854	0.119	7.128	0.000	0.99	0.98	53.128 (0.000)	2.40
	EXCH	–0.465	0.164	–2.826	0.198
	PCS	0.730	0.101	7.162	0.000
	LFOM	–0.110	0.081	–1.356	0.989
	AELE	–0.749	0.214	–3.495	0.006
	C	0.0132	0.010	1.326	0.217
AGR (H₂)	FDI	0.7280	0.1041	6.752	0.000	0.95	0.92	32.157 (0.000)	1.84
	EXCH	–0.153	0.040	–3.781	0.003
	PCS	0.3904	0.7388	0.528	0.336
	LFOA	–0.027	0.0078	–3.487	0.005
	AELE	–0.061	0.0337	–1.829	0.094
	C	3.959	0.222	17.803	0.000
SERV (H₂)	FDI	0.778	0.092	0.424	0.000	0.83	0.74	9.31 (0.003)	2.12
	EXCH	–0.888	0.061	–14.464	0.000
	PCS	–0.9633	0.09485	–10.156	0.000
	LFOS	0.826	0.115	7.147	0.000
	AELE	0.010	0.0107	0.097	0.924
	C	4.054	0.662	6.121	0.000
CO₂ (H₃)	MAN	0.254	0.017	14.703	0.000	0.97	0.90	13.73 (0.004)	2.01
	AGR	0.510	0.073	6.929	0.000
	SERV	0.813	0.059	13.585	0.000
	GDP	0.754	0.080	9.347	0.000
	AELE	–0.028	0.015	–1.883	0.118
	EXCH	–0.106	0.016	–6.537	0.001
	PCS	–0.062	0.0148	–4.190	0.008
	C	2.062	0.818	2.520	0.053

H₁: The results show that industrialization (manufacturing, agricultural and service sectors) positively and significantly influences Nigeria’s business and economic climate through job opportunities, higher production capacity, export potential, food security, increased rural income and service-oriented activities, signifying a shift towards a more diversified economy. Access to electricity, exchange rates and private sector credit also influence business and economic activities. The non-significant effect of access to electricity can be attributed to dependence on fossil energy sources, which reduces environmental quality and increases production costs. The negative effects of exchange rates and private sector credit indicate that rate fluctuations instigate economic and business instabilities. This result is consistent with the Kaldorian first and second laws of 1957, the big push theory and the empirical findings of Abner et al. (2023), Su and Yao (2017), Udo, Akpan et al. (2019), Udo, Udoh et al. (2019). This study recommends a balanced approach to inclusive economic development through industrial policies, technologies and innovative reforms to increase trade openness and attract FDIs. These results contradict the findings of Kapoor (2015) in India (Nasir et al., 2021) and Australia, and Nasrollahi et al. (2022) in MENA and OECD countries.

H₂: Factors influencing sectoral value addition in Nigeria. FDI and private sector credit significantly influence the value addition of the manufacturing and agricultural sectors. The negative influence of private sector credit on service sector value addition indicates that a 1% change in credit facilities decreases the investment capacity of firms for expansion and modernization because of interest rates, increasing cost of debt servicing, competition for investment and unfavourable economic conditions. These results are consistent with those previously reported (Kwakwa & Alhassan, 2018). The negative effect of exchange reduces sectorial growth and the employment rate. Inadequate access to electricity hinders production capacity and decreases environmental quality owing to heavy reliance on fossil fuels. The findings of Abner et al. (2021) and Udo et al. (2021) in Nigeria. In the long run, the findings support the results of Khan et al. (2022) in Morocco, Anwar et al. (2021) in G7 countries 1996–2018; Jahanger et al. (2022) and Udo et al. (2024) in MINT countries that technological innovation improves environmental quality. These results contradict the findings of Kapoor (2015) in India (Nasir et al., 2021) and Australia, and Nasrollahi et al. (2022) in MENA and OECD countries.

H₃ _: Industrialization affects environmental quality through CO₂ emissions. The findings indicate that a 1% increase in industrialization decreases environmental quality, particularly because manufacturing processes are energy-intensive and rely on fossil fuels for production in Nigeria. In addition, agricultural activities emit GHGs through deforestation mechanism and others. Economic expansion accompanied by increased industrialization, urbanization and energy consumption, particularly from fossil fuels and private sector credit, decreases environmental quality as economic agents in carbon-intensive industries acquire carbon-intensive machines. The decline in environmental quality in Nigeria can be attributed not only to the exploitation of lax environmental policies and compliance costs by multinational and domestic companies but also to human activities. These findings are consistent with those of previous studies (Ahmed et al., 2022; Anwar et al., 2020; Jermsittiparsert, 2021). The results of this study lend credence to EMT, which postulates that, in the long run, access to eco-friendly, cutting-edge technologies by economic agents can increase environmental quality, consistent with the findings of Abner et al. (2021), Anwar et al. (2021), Jahanger et al. (2022), Khan et al. (2022), Onifade et al. (2021) and Udo et al. (2021).

5. Conclusion and Managerial Implications

This study offers significant contributions by examining the multifaceted influence of industrialization on Nigeria’s business and economic landscape, along with its effect on environmental quality, with a focus on CO₂ emissions. This study captures both short- and long-run impacts using the ARDL bounce model, to provide nuanced insights often ignored by previous studies. Through the testing of these three hypotheses, this study distinguished the effects of industrialization across economic sectors, offering a detailed view of industrialization’s diverse impacts on Nigeria’s business and economic landscape.

The first hypothesis (H₁), industrialization encompassing (manufacturing, agricultural and service sectors), positively influences Nigeria’s business and economic climate. This study acknowledges the pivotal role of the service and agricultural sectors in enhancing economic growth. (H₂) Net FDI inflows positively and significantly influence sectoral (manufacturing and agricultural) value additions. This indicates that net FDI inflows enhance Nigeria’s technological adaptation and resilience to domestic economic shocks. The negative impact of private sector credit and exchange rates on service-sector value addition is attributed to inadequate access to electricity. The heavy reliance on fossil energy consumption adversely influences environmental quality, notably contributing to CO₂ emissions in Nigeria, which is considered most vulnerable to climate change due to geographical, economic, social and institutional vulnerabilities. (H₃) The link between environmental quality (CO₂ emissions), economic and business sustainability and climate change has received increasing global attention. Industrialization is acknowledged as a significant source of CO₂ emissions in Nigeria. Climate change is a quarrelsome issue, particularly in Africa, accounting for seven of the ten countries, including Nigeria, which is vulnerable to climate change.

The integration of economic and environmental theories, such as the EKC, PHH and PH, allows this study to bridge the gap between economic growth and environmental degradation debates. The findings of this study emphasize the need for targeted policies such as investment in and transition to greener renewable energy, facilitated through incentives, subsidies and regulations that promote sustainable industrial practices in Nigeria, thus, balancing industrial growth with environmental sustainability and aligning with the SDGs. Localized of this study insights embracing regional variations within Nigeria, emphasize the need for tailored policy interventions to specific local needs and conditions given Nigeria’s unique vulnerabilities, development trajectory, socio-economic and political dynamics at play. Furthermore, this study recommends stakeholders’ investment in climate-smart agricultural practices to reduce operational costs and increase sectoral efficiency. Carbon pricing and strict environmental regulations with high compliance costs for environmental policies can improve the environmental quality. Fostering public–private partnerships to promote sustainable business practices through the adoption of eco-friendly technologies is crucial.

The limitations of this study are evidence of evolving economic dynamics and global uncertainties, such as geopolitical events, insecurity challenges and pandemics. For future research, this study recommends prioritizing the influence of emerging technologies such as artificial intelligence, robotics, the Internet of Things (IoT) and renewable energy on both industrialization and economic growth within the Nigerian context. Longitudinal studies can offer valuable insights into the effectiveness of implemented policies and their sustained impact over time. This study deepens the understanding of the complex nexus between industrialization, economic growth and environmental quality in Nigeria, offering valuable insights for policymakers, academics and stakeholders interested in sustainable development and economic transformation in Nigeria.

Footnotes

Acknowledgement

We are sincerely grateful to the anonymous referees of the journal for their amazing scholarly suggestions to improve the quality of our article. Typical disclaimers apply.

Authors’ Contribution Statement

Ben Etim Udoh: Conceived the research idea and drafted the manuscript.

Joy Ifeoma Enemuo: Collected and contributed to manuscript writing and provided critical revisions.

Udo Emmanuel Samuel: Designed the study, conducted data analysis, reviewed literature and provided supervision throughout the project and revised the manuscript for clarity and accuracy.

Akpan Ededem Jack: Conducted data collection, contributed to data analysis and assisted in manuscript preparation and editing.

Ogogo Austin Ugar: Provided expertise in research design, contributed to data analysis and reviewed and revised the manuscript for clarity and accuracy.

Nkwonta Nkechi Caroline: Provided expertise in research design, contributed to manuscript drafting and reviewed and provided supervision throughout the project.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethical Declaration

The authors abide by all the ethics involved in this academic work and have not submitted it to any other journal.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Udo Emmanuel Samuel

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