Revealing the Dynamic Influence of Clean Energy Consumption on Economic Sustainability in Pakistan: A Pathway to Sustainable Development

doi:10.21203/rs.3.rs-3919896/v1

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Revealing the Dynamic Influence of Clean Energy Consumption on Economic Sustainability in Pakistan: A Pathway to Sustainable Development

https://doi.org/10.21203/rs.3.rs-3919896/v1

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Ensuring economic sustainability becomes a crucial consideration when examining the dynamic consequences of both clean and dirty energy consumption. This study seeks to analyze the influence of clean and dirty energy consumption, labor force, technological innovation, and capital formation on Pakistan's economic viability spanning from 1975 to 2022. To achieve this, we investigated these relationships utilizing a dynamic ARDL simulation model. The study findings indicate that clean energy consumption positively affects economic sustainability in both the short and long term. On the other hand, Dirty energy use has short-term benefits, it has long-term detrimental impacts on the viability of the economy. Although labor availability has a short-term negative impact on economic sustainability, it eventually loses significance. Economic sustainability is continuously enhanced by gross capital formation. To enhance economic sustainability, policymakers should prioritize the promotion of clean energy consumption because it constantly has a favorable effect over the long and short term. To reduce long-term drawbacks, a gradual switch from dirty energy is essential. Over time, labor development should change, and it's critical to promote capital formation. The aforementioned actions are intended to build a robust and sustainable future and strengthen Pakistan's economic sustainability.

Clean energy consumption

gross capital formation

labor force

technological innovation

economic sustainability

In the theoretical literature, many studies have been conducted on the role that energy plays in promoting economic sustainability and advancement (Khan and Sun, 2023; Lin and Samiullah, 2023; Lin and Ullah, 2023); many consider it the mainstay of the economy (Sun et al., 2023b). In view of the objectives for sustainable development and the issues facing the environment globally, it's imperative to reconsider the traditional approaches to energy production and use (Lin and Ullah, 2024; Raza and Lin, 2022). It is essential that everyone have access to affordable, dependable, modern, and environmentally friendly energy sources to achieve Sustainable Development Goal 7 (Ali et al., 2023; Hussain et al., 2023). The objective is to have widely available, affordable, reliable, and modern energy services by 2030. It also aims to significantly raise the proportion of clean energy in the world's energy mix and maintain pace with the advancement of energy efficiency worldwide (Raza and Lin, 2022; Wesseh et al., 2022). Aiming for more international cooperation to promote the development of clean energy research and technologies—including green energy, energy efficiency, and cutting-edge, cleaner fossil fuel technologies—Sustainable Development Goal 7 for 2030 (Ullah et al., 2023). In order to provide all developing countries with contemporary, sustainable energy services, it is also intended to encourage investment in clean energy technology and energy infrastructure (H. Zhang et al., 2024). This is especially crucial given the assistance extended to small islands, developing states, least-developed countries, and landlocked populations. Sustainable development entails attaining long-term progress in social, economic, and environmental aspects (Ma et al., 2023; Sun et al., 2023a; Yousaf Raza and Lin, 2023).

The mentioned circumstances necessitate a transition to cleaner energy production and the implementation of energy-efficient technologies (Li et al., 2024). Clean energy consumption holds importance in sustainable development since it is inherently sustainable and emits no greenhouse gases (Guo et al., 2023; Pata and Samour, 2022). A shift toward sustainable development has occurred in response to environmental degradation and climate change (Dr. Anwar Khan and Khan Bazai, 2023). Achieving sustainable development necessitates striking a balance between economic sustainability and social and environmental responsibility (Dhahri et al., 2024; Nguyen et al., 2023). According to the UN, sustainable development is any type of growth that takes care of current issues without jeopardizing the ability of future generations to meet their requirements. The generation and consumption of energy by conventional methods cannot be sustained throughout time.

Clean energy sources, including hydro, wind, solar, biomass, and geothermal, are viable alternatives to dirty energy (W.-J. Liu et al., 2024). The distinctive characteristics of these energy sources include their inherent capacity for self-renewal and their minimal ecological footprint (Chi et al., 2024). Clean energy sources, unlike fossil fuels, do not emit greenhouse gases during operation and are regarded as a more sustainable alternative. This attribute positions them as a cleaner option for meeting energy needs. Unlike fossil fuels, clean energy sources have the potential to keep producing energy indefinitely without the substantial greenhouse gas emissions associated with combustion (Bonnet and Olper, 2024; S. Liu et al., 2024; Yi et al., 2024). This matter gains increased significance within the context of the Paris Agreement, in which the world has pledged to restrict global warming to less than 2 degrees Celsius (Bano et al., 2022). Nations can decrease their environmental footprint by transitioning to more sustainable energy sources, thereby contributing to the fight against climate change (Yasmeen et al., 2022). Clean energy production and consumption also enable decentralized and widespread electricity distribution. Clean energy can be produced locally, reducing reliance on centralized energy systems, in contrast to fossil fuels, which are concentrated in particular areas and require substantial infrastructure for extraction and distribution. This increases resilience and energy security in regions susceptible to disruptions in the supply chain or unstable geopolitics (Bano et al., 2021; Hussain et al., 2021).

Pakistan's transition from conventional to clean energy consumption presents substantial opportunities to attain economic sustainability and mitigate the environmental repercussions linked with traditional energy sources. Among the Sustainable Development Goals (SDGs) being implemented in Pakistan are: "Transitioning to clean energy sources such as hydro, wind, solar, geothermal, and clean fuels like hydrogen and biofuels; implementing standards for technologies that reduce energy consumption at an affordable price; enhancing infrastructure; and upgrading technology to generate clean energy" (PBS, 2023). Establishing and sustaining clean energy production facilities within Pakistan can strengthen the economic base of the nation, provide new job opportunities, and promote regional economic expansion. Additionally, Pakistan would be better able to fulfill its obligations under the international climate agreement if it reduced its greenhouse gas emissions through the use of clean energy, which would support worldwide efforts to battle climate change (Qudrat-Ullah, 2022; Yousaf Raza and Lin, 2021).

These days, the necessity of expanding the variety of energy sources and implementing more ecologically friendly energy production techniques is becoming increasingly apparent, such as hydropower, geothermal energy, wind, solar, biomass, and hydropower (Erickson, 2017). The International Energy Agency forecasts a doubling of demand for clean energy by 2050, highlighting how important various energy sources are becoming to achieving economic sustainability. Investment in clean energy infrastructure can generate job opportunities across the production, installation, operation, and maintenance phases of the supply chain (IEA, 2022). The creation of jobs has the power to stimulate consumer spending, strengthen local economies, and advance economic growth as a whole (Adam et al., 2021). Moreover, transitioning to clean energy stimulates innovation and research and development (R&D) in clean technologies, fostering technological advancements, cost reductions, and enhanced efficiency (Yang and Khan, 2021). Progress in the clean energy sector brings benefits to both the clean energy industry and has ripple effects across other sectors, thereby contributing to more significant economic development and competitiveness (Khan et al., 2021b, 2021a).

Furthermore, establishing a low-carbon economy can improve energy security, decrease reliance on foreign fossil fuels, and decrease vulnerability to fluctuations in energy costs (Yanbin Li et al., 2023). Considering how urgent it is to address climate change, the adoption of clean energy presents a promising solution for countries like Pakistan and has the potential to result in significant economic development (Khan et al., 2020). Such a shift could promote their expansion in the rapidly expanding clean energy industry and help ensure a sustainable future. Moreover, reinforcing technological innovation in the energy sector will help provide a stable and regular supply of energy by integrating the production of clean energy into the current energy infrastructure. According to (Lin and Zhang, 2023), embracing and progressing new technological innovations can improve economic sustainability by lowering energy consumption, cutting costs, and mitigating environmental impact.

Furthermore, Pakistan's economy has traditionally relied heavily on its filthy energy resources, such as minerals, oil, and gas (Yousaf Raza and Lin, 2022). Furthermore, Pakistan's economy has traditionally relied heavily on its filthy energy resources, such as minerals, oil, and gas (Rehman et al., 2021). In this context, technological innovation plays a pivotal role, as advancements in technology can reduce the environmental impact of both clean and dirty energy.

Economic sustainability is a vital objective for nations worldwide, Pakistan included. Attaining this objective necessitates a delicate equilibrium between economic advancement, environmental preservation, and social well-being. The implementation and advancement of sustainable energy technologies, such as hydropower, wind, solar, and biomass, play a pivotal role in diminishing greenhouse gas emissions and reducing reliance on dirty energy production and consumption (Ganda, 2024; Zhang and Yu, 2024). By adopting these technologies, a resilient and sustainable energy infrastructure may be established, permitting the generation of sustainable energy. The diversification of energy sources in Pakistan and investments in energy efficiency can significantly bolster economic sustainability while concurrently mitigating environmental effects. Furthermore, the development of energy storage systems and innovative grid technologies has simplified the integration of clean energy sources into the grid, guaranteeing a stable and enduring electricity supply (Yao et al., 2021). This study investigates the crucial matter of the influence of energy transition on economic sustainability within IEA member countries. By utilizing Pakistan as a focal point, this research provides an academic addition to the theory of economic sustainability, accentuating potential interconnections among energy consumption, technological innovation, and economic sustainability.

This study examines how energy usage affects Pakistan's economic sustainability in light of the prior discussion. Research on how clean and dirty energy affect sustainability has historically typically concentrated on a small number of key aspects (Abbasi et al., 2022, 2021; Aftab et al., 2021; Ahmad et al., 2024; Aqeeq et al., 2023; Asghar Khan et al., 2022; Bilal et al., 2022; Khurshid, 2023; Liu et al., 2022; Mehmood et al., 2023; Sun et al., 2023a; Zhang et al., 2022). Nonetheless, our research clarifies the critical role that energy use and technological advancement play in achieving economic sustainability in Pakistan. Furthermore, even though it is commonly known that energy sources and use play a significant role in determining sustainability, prior studies have often overlooked crucial variables like energy consumption and technological innovation in their models. Utilizing a dynamic ARDL simulation model, this study introduces fresh perspectives into the dynamic influences of clean and dirty energy consumption, as well as technological innovation, on economic sustainability. The former encompasses essential energy consumption indicators, The latter, however, includes metrics related to sustainable economic growth. Principal component analysis (PCA) is used to create the index, which combines a variety of variables into a coherent framework for economic sustainability. This study enhances our comprehension of the intricate connections among energy consumption, technological innovation, and economic sustainability.

This paper is organized as follows in the sections that follow: A thorough evaluation of relevant literature is done in Section 2, with an emphasis on the knowledge gaps that still exist. The methods, data sources, and data analysis strategies used in this study are described in Section 3. In Section 4, the empirical results are interpreted. The report is finally concluded in Section 5, which makes policy recommendations based on the findings.

The relationship between energy consumption and economic growth has been the subject of much empirical investigation, and the body of literature pertaining to this topic has significantly expanded. The unpredictability of energy prices and the detrimental environmental effects of greenhouse gas (GHG) emissions from the burning of fossil fuels have created a major impetus for the adoption of clean energy in recent years. The enhanced sustainability and reduced carbon intensity of this energy system set it apart. The substantial increase in the adoption of clean energy can be mainly attributed to the considerable cost reduction in solar and wind power. The availability of energy is crucial for achieving economic growth (Tuna and Tuna, 2019). Nevertheless, present-day economic growth faces threats from a combination of factors. These include increased energy demand driven by global population growth, which has rapidly depleted traditional energy sources like natural gas, oil, and coal. The problems are exacerbated by growing energy expenses and the emissions of toxic gasses into the environment. According to (Dyrstad et al., 2019), there is a lack of energy accessibility, which indicates a lack of progress in reducing poverty and accelerating development. However, burning fossil fuels releases greenhouse gases (GHGs), which is why climate change is a problem. Consequently, an extended dependence on these fuels to meet a growing energy demand would only expedite the process of environmental degradation (Hanif et al., 2019). According to the U.S. Energy Information Administration's (EIA, 2023) report, global energy consumption is projected to increase by 48% by the year 2040, which emphasizes how urgently we need low-carbon substitutes.

Researchers continue to pay significant attention to the relationship between energy usage and economic sustainability. There is strong evidence in the literature review to support the positive relationship between these variables. Numerous scholarly inquiries provide a thorough analysis of the economic sustainability and utilization of clean energy sources (Jiang et al., 2020; Waheed et al., 2023; Q. Yang et al., 2022). Numerous studies have looked into the connection between economic development and hydroelectricity use and nuclear energy and economic sustainability (Azam et al., 2021a; H. Chen et al., 2023; Hao et al., 2022; Jin et al., 2022; Wang, 2022). These studies have used a variety of variables, different sampling strategies, and quantitative approaches to investigate various aspects of the states.

Several academic studies have looked into the relationship between nuclear energy and economic expansion, as exemplified by the studies conducted by Simionescu (2023) and Luqman et al (2019). (Pata et al., 2024) have explored various types of sustainable energy within the framework of the United States. L. Yang et al (2022), Liu et al (2022), and Alam et al (2016) have proposed four empirically testable hypotheses that aim to investigate the fundamental relationship between economic sustainability and clean energy consumption. The first, the growth hypothesis, suggests a positive correlation between energy consumption and economic growth, where energy is an essential input for an output. The conservative hypothesis, which is the second one, asserts a one-way causal relationship between economic expansion and energy use. Thus, policies aimed at reducing energy consumption are unlikely to negatively impact economic growth. The third, the feedback hypothesis, suggests a bidirectional causal relationship between economic growth and energy consumption. The last, known as "neutrality," asserts that the two ideas are independent of one another and that any action that affects one would not have an impact on the other. Feedback, conservation, economic growth, and neutrality are all included in these hypotheses.

Su et al (2022) investigate the response of the GDP of 116 nations to variations in the utilization of clean energy. Panel heterogeneity Dumitrescu and Hurlin causality assessment techniques, panel completely modified ordinary least squares, and panel co-integration tests are the tools the researchers use to examine the information. The findings suggest that both the consumption of clean and the top 10 CO2-emitting nations' economic growth is positively impacted by dirty energy (Azam et al., 2021b). Similarly to a prior research endeavor (Khan et al., 2019), an examination was undertaken to explore the relationship between the adoption of clean energy and economic progress. The analysis, which covered the years 1995 to 2021, concentrated on OECD and BRICS nations. For analysis, the present study utilized FMOLS and DOLS. The findings of the study reveal a positive association between the utilization of clean energy and economic sustainability (Addis and Cheng, 2023). Furthermore, it has been determined that the use of clean energy contributes to these countries' improved environmental conditions (Sun et al., 2022). The results from (Khan and Sun, 2023) study suggest a positive correlation between the utilization of clean energy sources and economic sustainability. According to the reference (Khan and Sun, 2023), nuclear energy sources have been identified as contributors to economic growth. The relationship between the consumption of clean and dirty energy and economic sustainability holds considerable significance (Sun et al., 2023b), especially within the context of Pakistan. With an abundance of energy resources, the nation has been attempting to balance environmental sustainability with economic growth (IEA, 2022). Economic sustainability may be greatly enhanced by the use of clean energy sources, such as hydroelectric, solar, wind, and nuclear power (Mendonça et al., 2020). This is accomplished by encouraging innovation and employment prospects in the clean energy sector, lowering greenhouse gas emissions, and enhancing energy security (Adebayo et al., 2023; Onifade et al., 2022).

Human capital, manifested through labor, plays a pivotal role in propelling economic growth (Khan et al., 2023). Economic activity's efficiency and inventiveness are directly impacted by labor productivity and skill levels (Dankyi et al., 2022). The World Bank (2018) states that the adoption of expertise development programs, education, and training is necessary to improve worker productivity and increase economic sustainability. It is widely acknowledged that promoting gender equality, good working conditions, and social safety nets can promote comprehensive economic growth, which in turn can reduce poverty and inequality. The capital component, which includes both material and immaterial resources, is essential to the promotion of economic sustainability. Adequate investment in infrastructure, technology, and research and development has been shown to enhance productivity and innovation, thus propelling economic growth (United Nations, 2023). The creation of public-private partnerships, financial accessibility, and the effectiveness of capital markets are necessary for the mobilization and deployment of money for sustainable projects (World Economic Forum, 2023).

Energy generation and consumption play pivotal roles in driving economic sustainability (Gershon et al., 2024), yet their environmental consequences pose challenges to achieving sustainability (Tao et al., 2023). According to Nuţă et al., (2024), in order to mitigate climate change and ensure economic growth, in 2018, the Intergovernmental Panel on Climate Change underlined how important it is to switch to greener and cleaner energy sources. Implementing green technology, clean energy infrastructure, and energy efficiency measures can increase job prospects, lower emissions, and improve energy security (IRENA, 2023). The way that labor, capital, and energy consumption are all related to achieving economic sustainability is demonstrated by their combined effect (Hu et al., 2023; Kim and Park, 2022; Satrovic et al., 2023). Following the (UNDP, 2011) and the (World Bank, 2018), increasing resource efficiency and worker productivity are necessary to achieve economic sustainability. Capital investments and energy changes, as suggested by Shao and Yang (2014), can help achieve this goal.

3.1. Theoretical settings

The theoretical foundation of this study lies in exploring the dynamic influence of clean and dirty energy consumption on economic sustainability, with a focus on the part that technological innovation plays. To assess the financial consequences of various energy sources, investigate the effects of patterns of energy usage, and recognize the ways in which technology influences these connections, a comprehensive understanding of the theoretical underpinnings of this relationship is necessary. Achieving long-term growth in economic sustainability necessitates efficient resource reuse, minimal environmental impact, and a dedication to enhancing social well-being (Zhang and Lin, 2024). Economic growth, environmental preservation, and social justice must be carefully balanced (Zhang and Qin, 2013). Several aspects must be considered when assessing how different energy sources affect economic sustainability, such as energy consumption, labor force participation, technical innovation, and gross capital formation.

The energy lifecycle, encompassing both production and consumption, involves various phases. Diverse energy sources, including renewable options like wind, hydro, and solar, as well as non-renewable sources like oil, coal, and gas, can be employed for energy generation. These resources are converted into electricity or heat to produce a usable form of energy. The term "energy consumption" refers to the ways in which end users, such as homes, businesses, and the transportation industry, use the energy produced to meet different demands, like manufacturing, lighting, heating, and transportation. Energy production concentrates on the supply side of the equation, whereas energy consumption shows how people and organizations integrate energy resources into their daily operations and activities.

The process of producing energy requires the use of both non-renewable and renewable resources. On the other hand, energy consumption refers to how end consumers use this energy. In contrast to non-renewable sources, clean energy possesses the distinctive feature of self-replenishment through natural means such as hydroelectric, geothermal energy, solar, and wind. By encouraging technical innovation, reducing greenhouse gas emissions, increasing energy security, and diversifying energy sources through a variety of strategies, clean energy supports economic sustainability (Algarni et al., 2023). Examining the changing effects of renewable energy on economic sustainability requires a thorough examination of cost competitiveness, scalability, and the seamless integration of clean energy into the existing energy system (Wang et al., 2022). On the other hand, dirty energy harms the environment and is dependent on limited resources like coal, oil, and natural gas (Jiang et al., 2022). Historically, economic development has been propelled by the use of dirty energy; nevertheless, its long-term sustainability is a cause for concern (Yu et al., 2023). Determining the dynamic consequences of dirty energy on economic sustainability requires a detailed examination of the effects on the geopolitical repercussions, value volatility, environment, and prolonged energy security (AlNemer et al., 2023). To grasp the potential advantages and challenges associated with transitioning to clean energy sources, it is crucial to comprehend the shift from dirty to clean energy sources (Hao et al., 2023).

Technologies improve energy generation methods' sustainability, efficiency, and environmental friendliness. This innovation aims to improve overall performance, reduce emissions, and maximize resource usage by introducing cutting-edge technologies into the energy-producing process. Technological innovation intertwines economic sustainability with clean energy generation and consumption. Technological developments increase the economic viability and scalability of clean energy through its effective integration, storage, and management (Gong et al., 2023). Several facets of technological innovation contribute to economic sustainability, encompassing measures for energy efficiency, technologies for emissions reduction, and demand-side management. When taken as a whole, these factors reduce waste, energy use, and environmental effects (Tao et al., 2023). When evaluating the dynamic impact of technical innovation, it is essential to take into account elements like affordability, technological maturity, support from the government, and chances for invention and implementation.

By combining these theoretical elements, this research aims to offer insights into the dynamic effects of clean and dirty energy use on economic sustainability. It also focuses on assessing the impact of stakeholder participation and policy frameworks on the dynamic role of technological innovation. For Pakistan to make informed decisions, develop policies, and promote a resilient and sustainable energy future, a full understanding of these processes is essential. The study's theoretical framework is shown in Fig. 1.

Figure 1. Theoretical framework of the study

3.2. Data

This study evaluates the dynamic effect of technical innovation and the dynamic impact of clean and dirty energy consumption on economic sustainability using time series data covering the years 1975–2022. Secondary sources provide observations for the factors that are dependent and independent. The World Development Indicator (WDI, 2022) and OurWorld in Data (OWID, 2023) are examples of these secondary sources. Economic sustainability (ECOS) is the dependent variable, and the independent variables or predictors are labor force (LF), technological innovation (TI), clean energy (CEC), dirty energy consumption (DEC), and gross capital formation (GCF). Table 1 presents the sources, units of measurement, and abbreviations for these variables, all of which have been carefully reviewed in a comprehensive literature study. The applied methodological framework for this study is further depicted in Fig. 2 below.

Table 1

Detail of selected parameters
Parameters	Symbol	Unit	Sources
“Inflation, consumer prices	ECOS	annual %	WDI
Taxes on income, profits and capital gains		% of revenue	WDI
Population		Total	WDI
Final consumption expenditure		Current US$	WDI
Exports of goods and services		annual % growth	WDI
Unemployment, total		% of the total labor force	WDI
Gross domestic product (GDP)		current US$)	WDI
Clean energy consumption	CEC	A sum of nuclear and hydro energy consumption (TWh)	OWID
Dirty energy consumption	DEC	Total energy consumption in coal, oil, and natural gas, divided by total population (kWh)	OWID
Labor force	LF	Population ages 15–64 (% of the total population)	WDI
Gross capital formation	GCF	constant 2015 US$	WDI
Technological innovation	TI	No. of patent applications by residents	WDI”

Table 1. Detail of selected parameters

Figure 2. Methodological framework

3.3. Economic modeling

Using Principal Component Analysis (PCA), we produced the economic sustainability index by combining variables including GDP, exports of goods and services, income tax, population, inflation, and final consumer expenditure. Table 2 provides a full breakdown of the main component analysis results, and Fig. 3 displays the Biplot representation. The eigenvalues of each component indicate the proportion of variance captured by the original data. Notably, PC1, which represents 43.19% of the total variance, possesses an eigenvalue of 3.02, significantly exceeding the noise level. In this study, a single primary driver is associated with seven economic sustainability indicators. The study uses a multivariate production function to regulate labor and capital while examining the dynamic influence of both clean and dirty energy use. The empirical model is outlined as follows:

Table 2

PCA analysis
	“Eigenvalues	Percentage of Variance	Cumulative”
1	3.024	43.194%	43.194%
2	1.602	22.884%	66.078%
3	1.140	16.292%	82.371%
4	0.769	10.991%	93.362%
5	0.405	5.788%	99.150%
6	0.059	0.847%	99.997%
7	0.000	0.003%	100.000%

$$ECOS=f(CEC,DEC,LF,GCF,TI)$$

All variables except economic sustainability have been transformed into their natural logarithms. Eq. 2 shows the logarithmic form of Eq. 1.

$$ECOS=f(LNCEC,LNDEC,LNLF,LNGCF,LNTI)$$

Table 2. PCA analysis

Figure 3. Principle component analysis’s Biplot

3.4. Econometric modeling

Jordan and Philips (2018) created the estimation of the variables' short- and long-term coefficients using the ARDL simulation model, a novel dynamic version. In addition to estimating the long- and short-term correlations between the variables, the model can automatically produce graphs that display both positive and negative changes in the variables. To ascertain the proper order of integration, stationarity tests must be carried out prior to advancing with the new dynamic ARDL simulation approach. In light of this, the augmented Dickey-Fuller (ADF, 1979) test, the Phillips-Perron (PP, 1988) test, and the DF-GLS detrended unit root test (Vougas, 2007) are the typical unit root tests we use. Furthermore, we evaluate the cointegration connection using the ARDL bounds test. The ARDL bounds (Pesaran, 2001) test approach allows for the testing of variables that exhibit different integration orders (i.e., I(0) or I(1)), which distinguishes it from the traditional cointegration test. The advantage of this method is that it leads to valid conclusions in investigations involving limited sample sizes. In addition, it allows for the calculation of both short-run and long-run coefficients. After confirming the long-term relationship, DARDL chooses the coefficients following Eq. 3.

$$\begin{gathered} \Delta ECO{S_t}={\lambda _0}+{\delta _0}ECO{S_{t - 1}}+{\gamma _1}\Delta CE{C_t}+{\delta _1}CE{C_{t - 1}}+{\gamma _2}\Delta DE{C_t}+ \hfill \\ {\delta _2}DE{C_{t - 1}}+{\gamma _3}\Delta L{F_t}+{\delta _3}L{F_{t - 1}}+{\gamma _4}\Delta GC{F_t}+{\delta _4}GC{F_{t - 1}} \hfill \\ +{\gamma _5}\Delta T{I_t}+{\delta _5}T{I_{t - 1}}+{\Omega _t} \hfill \\ \end{gathered}$$

Before estimating the model, a thorough empirical examination of the summary statistics of the data series was conducted, as indicated in Table 3. The average values of the variables are displayed in the results, with Gross Capital Formation (GCF) exhibiting the highest mean among them. Regarding the distribution shape characterized by skewness, ECOS, LF, and TI display a right-skewed pattern, while CEC, DEC, and GCF exhibit negative skewness. Furthermore, the kurtosis statistics demonstrate that every variable—aside from CEC—displays a platykurtic distribution, which denotes a distribution with a low kurtosis or a short tail. Additionally, the Jarque-Bera test results demonstrate that the residuals for each variable have a normal distribution.

Table 3

Statistical description
Descriptive statistics	ECOS	LNCEC	LNDEC	LNLF	LNGCF	LNTI
Mean	-1E-08	6.0577	7.8357	4.0029	24.0295	20.0318
Median	0.0692	6.137	7.8942	3.9878	24.0499	19.6626
Maximum	1.9595	6.4358	8.3017	4.0802	24.8345	21.7915
Minimum	-1.699	5.1988	7.1915	3.9523	22.9454	18.5876
Std. Dev.	1	0.2482	0.3256	0.0401	0.5271	0.977
Skewness	0.0758	-1.3934	-0.497	0.5001	-0.4163	0.2209
Kurtosis	2.2974	5.0651	2.0812	1.7568	2.2352	1.6189
Jarque-Bera	1.0333	24.0626	3.6649	5.0914	2.5565	4.2053
Probability	0.5965	0	0.1601	0.0784	0.2785	0.1221

Table 3. Statistical description

Moreover, Fig. 4 displays box plots that present a summary of the parameters' statistics. Figure 4 also provides descriptive statistics for Pakistan spanning the years 1985 to 2022. The box and scatter plots visually represent the 10th, 25th, 75th, and 90th percentiles of the data. The whisker cap corresponds to the 1st and 99th percentiles, while dots on the plots signify maximum (min) and minimum (cap) values.

Figure 4. Boxplots of the selected parameters

Furthermore, Fig. 5 illustrates the correlation among the variables. The accompanying figures present details on the Pearson correlation coefficients, histograms, and bivariate scatter plots. In the upper right corner of Fig. 5, the triangular region depicts the bivariate Pearson correlation coefficients ranging between − 1 and + 1. Strong positive or negative correlation is indicated by values that are near to + 1 or -1, respectively. Figure 5 reveals a positive correlation among ECOS, CES, and GCF, whereas ECOS, DEC, LF, and TI exhibit a negative correlation.

The statistical analysis using Pearson's correlation coefficient indicates a significant and robust correlation among all variables in the study. Notably, there is a strong correlation observed between ECOS, CEC, DEC, LF, GCF, and TI. The scatter plot in the lower-left corner of Fig. 5 visually represents the relationship between two variables, and the strength and direction of this correlation are elaborated upon in this section. Specifically, an increase in CEC and GCF is associated with a corresponding increase in ECOS, while the relationship is reversed for DEC, LF, and TI. To further illustrate the distributional properties of these variables, Fig. 5 includes a diagonal plot.

Figure 5. Presentation of the correlation structure graphically

Utilizing various stationarity tests, the stationarity analysis for each variable is shown in Table 4. The results indicate that there is statistical significance for all variables in the first-order difference, which suggests that the variables are not stationary at the second-order integral I(2). These results validate the absence of stationarity at the second-order integration level of the series analyzed in this work. Consequently, the ARDL model can efficiently and reliably employ orders I(0) and I(1) to estimate the relationship between the variables.

Table 4

Stationarity test
Variables	Augmented Dickey-Fuller (ADF)		Phillip Perron (PP)		DF- GLS Residuals
Variables	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
ECOS	-2.7508	-7.5546	-2.8401	-7.5546	-2.3948	-7.6219
	(0.073/-2.925)	(0.000/-2.926)	(0.061/-2.925)	(0.000/-2.926)	(-1.947)	(-1.947)
LnCEC	-3.8427	-10.7709	-3.6638	-12.7744	-1.1566	-10.8823
	(0.004/-1.925)	(0.000/-2.926)	(0.007/-1.925)	(0.000/-2.926)	(-1.947)	(-1.947)
LnDEC	-2.1131	-5.4707	-2.1131	-5.7073	0.4571	-2.2368
	(0.241/-1.925)	(0.000/-2.926)	(0.241/-1.925)	(0.000/-2.926)	(-1.947)	(-1.947)
LnLF	-0.4189	-4.7764	-0.0727	-4.7757	0.4463	-4.7798
	(0.897/-2.925)	(0.000/-2.926)	(0.946/-2.925)	(0.000/-2.926)	(-1.947)	(-1.947)
LnGCF	-1.9898	-5.1646	-1.8438	-5.2385	0.3671	-5.1438
	(0.290/-2.925)	(0.000/-2.926)	(0.355/-2.925)	(0.000/-2.926)	(-1.947)	(-1.947)
LnTI	-1.2403	-13.4448	-1.6708	-13.6443	-0.8081	-4.0381
	(0.649/-2.925)	(0.000/-2.926)	(0.439/-2.925)	(0.000/-2.926)	(-1.947)	(-1.947)
Note: (p-scores/t-stat)

In the ARDL model, both independent and dependent variables are permitted to have varying lag lengths. As shown in Table 5, some tests were run to find the best lag selection criteria. The best lag duration for our model was determined by applying the Akaike Information Criterion (AIC). When the AIC values were compared, the lowest AIC value was obtained with three lags in time, indicating that it made sense for the model. Thus, a lag time of three is judged suitable for capturing the dynamic changes in the variables in our analysis based on the AIC results. To ensure the reliability and stability of the ARDL coefficient, several post-estimation tests were conducted, and the diagnostic results are presented in Appendix Tables A1 through A3.

Table 5

Bound test
K		1%		5%		10%		p-value
		I(0)	I(1)	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
F	5.485	3.425	5.143	2.41	3.791	1.982	3.212	0	0.002
t	-4.1	-2.68	-4.693	-1.968	-3.88	-1.611	-3.475	0.003	0.032
Note: Lower and upper bounds are indicated by I(0) and I(1)

Apart from the static test, the assessment of long-run cointegration was conducted using the bound test, and the outcomes are presented in Table 6. It's crucial to remember that the presence of a cointegrating relationship doesn't necessitate a p-value below 0.05 for the constraints test at a 5% significance level. Considering this, a thorough examination of the long-run relationship would be fitting.

Table 6

Model lag selection
Lag	LogL	LR	FPE	AIC	SC	HQ
0	84.6751		1.30E-09	-3.42E + 00	-3.33131	-3.18214
1	347.577	525.8	6.90E-14	-13.286	-12.6605	-11.6163*
2	406.277	117.4*	2.8e-14*	-14.2729*	-13.1114*	-11.1722

Table 4. Stationarity test

Table 5. Bound test

Table 6. Model lag selection

To assess the short and long-run relationship, we utilized the dynamic ARDL model proposed by Jordan and Philips (2018). The model outcomes are detailed in Table 7. It reveals that the error correction term is -0.32, displaying a negative and statistically significant value. This suggests that the variables gravitate towards a normal long-term relationship over time, with an adjustment rate of 32%. The coefficients related to CEC exhibit notably favorable outcomes in both the short and long run. Conversely, the study findings imply that the increase in ECOS can be attributed to CEC. Specifically, the study indicates that a 1% increase in CEC corresponds to a 0.78% rise in ECOS in the short term and a 0.89% increase in the long term. A positive relationship between clean energy consumption and economic sustainability has been demonstrated by previous studies conducted by Qing et al (2024) and Khan et al (2024). Because they generate jobs, the development and use of clean energy has a positive effect on economic sustainability (Wali et al., 2023; Yang and Long, 2024), fueling economic growth, enhancing cost-effectiveness, bolstering energy security, driving technological innovation, advocating sustainable development, and mitigating environmental impacts (M. Zhang et al., 2024). It is essential to incorporate clean energy into the energy mix to ensure a sustainable future and build an economy that is stable and emits few emissions (Wang et al., 2024).

Table 7

Dynamic ARDL outcome
Parameter	Dynamic ARDL
Parameter	Coefficients	t-stat	p-value
Short-run:
∆LnCEC	0.7869	2.52	0.016**
∆LnDEC	0.7621	1.4	0.122
∆LnLF	-0.4906	-2.67	0.011**
∆LnGCF	0.4599	2.52	0.016**
∆LnTI	0.3233	2.59	0.014**
Long-run:
LnCEC	0.8931	2.92	0.006***
LnDEC	-0.3276	-2.33	0.026**
LnLF	-0.5282	-0.79	0.432
LnGCF	0.4012	2.12	0.041**
LnTI	-0.7148	-2.2	0.034**
Cons	-0.318	-2.92	0.006***
ECT	-0.332	-3.04	0.004***
Adjusted R2	0.8195
R2	0.8976
RMSE	0.6397
Simulation	5000
Note: , , and * indicates 10%, 5%, and 1% level of significance*

The Dirty Energy Consumption (DEC) coefficient is positive in the short run but not statistically significant; in the long run, though, it turns negative and becomes statistically significant, showing a significant dynamic relationship. The results demonstrate that over the long term, a 1% increase in DEC is associated with a 0.32% decrease in ECOS. The short-term statistical significance of the relationship between dirty energy consumption (DEC) and economic sustainability is not established, but it shifts with time to become significant in the long term due to its negative effects. While not statistically significant, the short-term impact of filthy energy consumption on economic activity may be favorable. However, in the long run, environmental sustainability concerns make the negative impact of filthy energy consumption on economic sustainability clear. Pollution and resource depletion are two undesirable externalities linked to the use of dirty energy sources (Jiang et al., 2022). It additionally indicates the escalating significance of the shift towards clean energy, propelled by alterations in policies, advancements in technology, and the rising cost competitiveness of clean energy (Das et al., 2022).

In addition, the labor force coefficient has a statistically significant negative influence on ECOS in the short term, although this effect loses importance with time. The short-term detrimental and statistically significant effect of LF indicates that the availability of labor has an adverse effect on economic sustainability. This may be attributed to imbalances in the labor market, skill mismatches, or inefficiencies in the utilization of labor (Achuo et al., 2023). Short-term labor surpluses or shortages have the potential to negatively impact the economy's sustainability. LF does not, however, have a detrimental long-term effect on economic sustainability. A number of variables, including resource availability, legislative changes, and technology improvements, may have an impact on long-term economic sustainability. The positive coefficient of GCF significantly influences ECOS, indicating that, based on empirical findings, a 1% rise in GCF is correlated with increases in short-term and long-term ECOS of 0.45% and 0.40%, respectively. The results of the study align with those of (Ying Li et al., 2023), (F. Chen et al., 2023), and (Subhan et al., 2024), which showed that economic sustainability and gross capital formation were positively correlated in the G20, a group of middle-income nations, and Canada, respectively. Building up capital is essential to promoting economic sustainability. Infrastructure, productivity, technology, and machinery capital investments can all help to increase output and spur economic growth. Capital investment in infrastructure, technology, machinery, and productivity can contribute to the improvement of output and economic growth (Zaman et al., 2021). Businesses grow because they generate employment possibilities, which lowers unemployment and improves people's quality of life overall (Etokakpan et al., 2020). In addition, investing in educational and training programs to develop human capital can increase output and promote economic expansion. Through this investment, people can learn the necessary skills that will increase their employability in higher-paying jobs (Li et al., 2022). A robust capital base facilitates investments in public infrastructure and job creation, thereby fortifying the endurance of economic expansion. Empirical findings indicate that capital exerts a substantial and positive influence on economic sustainability, influencing all sectors and fostering enduring growth and stability.

Furthermore, the study found that the technical innovation coefficient has a statistically significant short-term positive influence on economic sustainability and a statistically significant long-term negative impact. According to the empirical data, economic sustainability levels rise by 0.32% for every 1% increase in the technological innovation index in the short term, while in the long term, economic sustainability declines by 0.71% for every 1% increase in the technological innovation index. Technological innovation can, in the near run, minimize energy prices, improve economic activity, and create jobs in order to support economic growth and productivity. However, conventional energy sources like fossil fuels may experience long-term disruptions, which could have negative consequences including job losses and economic difficulties for the affected areas. Government budgets and the viability of the economy as a whole may suffer as a result of the costs involved in switching to new energy technology (Liu and Lin, 2024). Maintaining long-term economic viability requires weighing the benefits and drawbacks in order to facilitate a seamless and inclusive transition.

Table 7. Dynamic ARDL outcome

Dynamic ARDL simulations were used to evaluate the various effects of labor force participation, capital formation, energy consumption, and technological innovation on economic sustainability. The variables under consideration in these simulations were subjected to 10% positive and negative counterfactual shocks between 2022 and 2062 (a period that coincides with the estimation of economic sustainability). A 10% change in estimated clean energy consumption has an effect on economic sustainability in the second stage, which in turn affects the acceleration of economic sustainability in the next stage, as Fig. 6 shows. Conversely, Fig. 7 shows how a 10% positive or negative shock to the estimated amount of dirty energy consumed could affect the second stage's economic sustainability but ultimately lead to a reduction in that sustainability. Both good and bad 10% shocks could have an impact on labor in the second stage of economic sustainability, as shown in Fig. 8, improving the situation in later phases. Nonetheless, the dynamic ARDL simulation depicted in Fig. 8 indicates that long-term economic sustainability is not considerably impacted by the 10% shocks, both positive and negative, to the anticipated workforce. Figure 9 illustrates how the predicted 10% positive and negative shocks in capital production are expected to affect the second stage of economic sustainability, and the future trajectory is expected to follow the capital formation trend. On the other hand, Fig. 10 shows that economic sustainability is predicted to be impacted in the second stage but to fall in the stages that follow due to the 10% positive and negative shocks to technological innovation.

Figure 6. Change in the prediction of CEC

Figure 7. Change in the prediction of DEC

Figure 8. Change in the prediction of LF

Figure 9. Change in the prediction of GCF

Figure 10. Change in the prediction of TI

Both clean and dirty energy usage have dynamic impacts that raise questions about economic sustainability. Achieving economic sustainability requires striking a careful balance between social progress, environmental protection, and economic expansion. In order to investigate the complex interaction between clean and dirty energy consumption, technological innovation, and economic sustainability, this study uses dynamic Autoregressive Distributed Lag (ARDL) simulations. The findings reveal several noteworthy outcomes. First of all, the data demonstrates that using clean energy both now and in the future will significantly improve economic sustainability. Second, there is a complex dynamic at play when it comes to how the use of dirty energy affects economic sustainability. Although statistically insignificant, the short-term effects of dirty energy consumption on economic activity are positive. On the other hand, over time, it becomes clear that using dirty energy has a negative impact on economic sustainability. This underscores the need to shift focus towards clean energy, driven by concerns related to both environmental conservation and economic sustainability. Additionally, the study finds that labor has a short-term detrimental effect on economic sustainability, which may be related to labor market imbalances or inefficiencies.

Conversely, over an extended period, the influence of labor on economic sustainability diminishes, indicating that factors such as resource availability and technological advancements gain more prominence. The findings demonstrate that gross capital development has a substantial and favorable impact on economic sustainability over the long run. In light of these discoveries, capital investment emerges as crucial for fostering economic growth and sustainability by enhancing productivity, generating employment opportunities, and developing infrastructure. Over time, technological progress and the availability of resources become increasingly impactful. Lastly, the study explores the ramifications of technological innovation on economic sustainability. The results show favorable short-term benefits, indicating that technology innovations boost economic activity and lower energy costs. On the other hand, the long-term negative effects that are expected are a result of the old energy businesses being disrupted and the price of transferring to new technology.

The results of the study serve as a foundation for a number of policy suggestions meant to improve economic sustainability. First and foremost, the Pakistani government must actively support and encourage the use of clean energy. Implementation of policies, like incentives, tax exemptions, and feed-in tariffs for renewable energy projects, can contribute significantly to this objective. Pakistan's long-term economic viability can be strengthened by moving toward greener and more sustainable energy sources. Second, it is imperative to allocate resources towards the advancement of the labor market. Even if labor availability may not have a significant impact on long-term economic sustainability, resolving skill mismatches, improving labor utilization effectiveness, and creating a flexible and responsive labor market are essential to the industry's short-term survival. Important first steps toward accomplishing this include encouraging worker mobility, entrepreneurship, and support for programs related to education and training.

Furthermore, promoting gross capital formation is essential to the advancement of sustainable economic growth. Infrastructure, technology, and machinery investments must be given top priority by the government. Encouraging capital investments can be accomplished by putting tax incentives into place, working with the private sector on infrastructure projects, and giving research and development-specific money. By fostering job creation and raising productivity, capital formation supports economic growth and sustainability. The study emphasizes how crucial it is to handle the adoption of new energy technology with efficiency. Pakistan must develop a comprehensive plan and policy that successfully strikes a balance between immediate economic benefits and long-term sustainability. This entails establishing retraining and employment transition programs, offering assistance to areas and sectors impacted by the shift to new energy sources, and making sure that the transition is inclusive for all parties involved. Pakistan may mitigate future economic obstacles and ensure long-term economic viability by skillfully handling this transition. Additionally, to foster sustainable economic development, there is a need to actively promote innovation and invest in research and development (R&D). Increasing financing for research and development (R&D), encouraging industry-government collaboration, and creating regulations that support technology transfer and commercialization are all essential to improving the success of technology transfer and commercialization. Supporting entrepreneurship and innovation can result in the creation of new products, services, and technologies, which in turn can promote economic growth and sustainability. Pakistan must prioritize its involvement in international initiatives for climate change mitigation, clean energy, and sustainable development, especially in light of the global character of environmental and economic concerns. The government of Pakistan holds a vital position in attaining sustainability goals through the exchange of knowledge and adopting best practices. Policymakers should seriously consider these recommended policies to improve economic sustainability. Supporting renewable energy will help Pakistan foster economic growth and prosperity while building a more resilient and sustainable future, investing in the development of the labor market, encouraging gross capital formation, encouraging creativity and R&D, transitioning to fresh energy innovations, and fortifying global partnerships.

This study has certain limitations that warrant consideration. Primarily, the research is exclusive to Pakistan, which implies that the findings may not be universally applicable to nations with varied energy and economic profiles. The analysis relies on present-day data and involves certain assumptions. Future research endeavors could broaden the scope by incorporating more countries and utilizing more intricate data sets to gain a deeper understanding of localized impacts. It would be helpful to look into how the intricate interaction between energy use, technology, and economic sustainability is affected by changes in policy and technological advancements. Additional investigation into the ecological and societal facets of sustainable economies, together with the contribution of technology and energy usage, would be highly advantageous.

Author Contribution

Muhammad Ali: Conceptualization, Methodology, Formal analysis, Software, Writing - original draft, Investigation, Writing - review & editing. LIU Xiaoying: Supervision, review, validation, and resources. Anwar Khan: Data curation, review & editing

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Revealing the Dynamic Influence of Clean Energy Consumption on Economic Sustainability in Pakistan: A Pathway to Sustainable Development

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