Green horizons: Synergized green technological innovation and green energy consumption on ecological degradation

Abstract

The ecological environment is at great risk due to economic activities owing to investments, rapid industrialization, and energy resources depletion, which pose significant environmental challenges globally. Recent research has emphasized on the crucial role of green energy consumption (GEN) and green technology innovation in attaining environmental sustainability. However, there are still areas that need to be addressed. This research study investigates the role of GEN, green technology innovation, and foreign direct investment while controlling the effect of economic growth (EGR) and trade openness level on the ecological footprint (EFP) using data from 1999 to 2022 for Pakistan. We used diverse econometric methodologies to ensure the data consistency. We examine the long-term relationships among the study variables using fully modified ordinary least-squares and canonical co-integrating regression. The results revealed that green technology innovation, and GEN emerge as pivotal in reducing environmental degradation. Conversely, foreign direct investment and EGR increased the EFP, validating the pollution haven hypothesis in Pakistan. These findings highlighted the significance of green technology innovation that increased GEN to address ecological degradation. These findings contribute to more extensive discussions on environmental sustainability and development of the green horizon, particularly in the context of developing countries like Pakistan.

Keywords

ecological footprint green energy consumption green technological innovation foreign direct investment Pakistan

Introduction

The pursuit of sustainable solutions has become a significant and imminent problem for economies across the globe, considering the inescapable environmental challenges. The ecological degradation of the climate of the mother Earth and the subsequent occurrence of climate change events have been substantially influenced by human activities across multiple sectors, with a surging inflow of investment and economic activities.^1,2 Both the millennial and the sustainable development agenda share the objective of achieving environmental sustainability by incorporating sustainable development concepts into national policies, investments, and environmentally friendly innovations.³ Thus, discussions have surfaced concerning environmentally sustainable practices, sustainable investment, and sustainable economic development.

The past economic development paradigm centered on rapid economic gain through deteriorating energy resources, leading to significant envivonmental degradation,⁴ rendering it incapable of effectively sustaining long-term economic development goal considering the future of mother earth. Around 75% of all greenhouse gas (GHG) emissions can be linked to energy consumption worldwide, with fossil fuels contributing to over 44% of these emissions.⁵ The UN SDGs project a 41–109% increase in energy-related CO₂ emissions by 2030.⁶ In view of these two alarming challenges, it is imperative to wholeheartedly embrace a sustainable development approach through decreasing resource utilization and protecting the ecological environment without compromising sustainable development. Multiple strategies, including the implementation of GTI, investment in environmentally friendly industries, transition to GEN sources, and the enactment of emission-reducing rules and policies, can accomplish this aim.⁷ Therefore, there is a greater need to increase investment for environmentally vaiable projects with the goal of reducing ecological deterioration.^8,9

Moreover, GEN is regarded as an indispensable strategy to attain net-zero emissions and restrict the rate of global warming to 1.5°C.^10,11 Recent research indicates that the majority of economies are increasingly dependent on GEN sources for their energy requirements.¹² The International Atomic Energy Agency (IAEA) estimations revealed wind and sunshine energy capacity has surpassed by 226 GW in 2021 solitary, with the solar power currently having total share of 10% of the global energy supply, surpassed the share of nuclear energy.¹³ Current proposals predict that investments in GEN will result in a 70% drop in worldwide CO₂ discharges by 2050. In 2021, a significant rise of around 5.1 exajoules (EJ) in renewable primary energy, which includes biofuels but excludes hydro, will achieve this reduction.¹⁴ However, integrating GEN with domestic energy infrastructure poses challenges, particularly for economies hindered by heterogeneous industrial structures, financial constraints, investment limitations, and technological developments.

Additionally, GTI is a crucial prerequisite for adequately addressing climate change and other ecological problems, as well as guiding the world toward limiting pollution.¹⁵ Emerging GTI strategies and measures have the potential to improve the development and use of GEN technologies in an energy system to reduce ecological degradation. GTI's primary feature is its capacity to enhance energy efficiency and facilitate the advancement of cost-effective, environmentally friendly solutions.¹⁵ The integration of GTI into the manufacturing process enhances the efficiency of factors’ performance. This, in turn, facilitates the shift to more sustainable sources and contributes to the economic benefits of investing in innovation investment. Many emerging economies are currently prioritizing the development and deployment of GTI. The European Union and ASEAN countries has been collaborating to implement the EU-ASEAN Dialogue on GTI Planning using the improved Regional EU-ASEAN dialogue instrument. Consequently, GTI is a specific sector of investment that is progressively enhancing the use of GEN. Despite GTI has made a significant contribution to energy systems, policymakers still have concerns over the initial investment involved in developing renewable energy projects.¹⁶

Due to several significant factors, Pakistan is the central point of our research. First, Pakistan, as a developing economy, is exceptionally vulnerable to the effects of climate change; As Pakistan's existing development objectives depend primarily on natural resources, while industrial operations that rely extensively on polluting energy sources are increasing the country's ecological impact. Pakistan has had catastrophic floods and droughts over the past two decades, causing serious effects across socio-economic and ecological domains. Global climate risk index data specifies that Pakistan is the fifth most exposed country to climate change.¹⁷ Ullah et al.¹⁸ reported that Pakistan experienced a total of 152 severe weather events over the previous ten years, resulting in economic losses totaling US$3.8 billion. Pakistan's per capita EFP has increased from 0.5 to 0.99 gha between 1980 and 2020.¹⁹ GHG, climate change, and increased water pollution have exacerbated the EFP issue in Pakistan. Along with other contributing factors, the increasing inflow of foreign direct investment (FDI) has increased industrialization, and the population of over 222 million has led to a rise in energy demand. Also, approximately 54% out of the total 70% of the rural populace that lacks access to electricity resides in rural regions. Fossil fuel resources primarily meet the country's high energy demand from thermal power, which intensifies environmental degradation. In 1971, Pakistan consumed 35% of its total energy from imported fuels, However, for the last two decades, energy consumption, including bioenergy, has nearly doubled at an average growth rate of 4.4%.¹⁴ Within this particular framework, GEN provides an opportunity to ensure energy stability and avert an energy crisis. Pakistan aims to fulfill 30% of its energy supply from renewable sources by 2030. Pakistan possesses an untapped energy potential of approximately 60 GW from hydro, 40 GW from solar, and 346 GW from wind energy sources.²⁰ Furthermore, the inflow of FDI that results in the development and utilization of GTI has the potential to significantly facilitate the shift toward GEN and promote the country's dual goals of economic development and environmental sustainability. Hence, it is critical for comprehensive research on the effects of GEN, GTI, and FDI inflow in Pakistan to assess their influence on ecological degradation. Pakistan's vulnerability to climate change necessitates urgent and effective mitigation strategies such as adoption of development of GEN and GTI to reduce ecological degradation and join the global effort to attain environmental sustainability. The intricate dual nature of this relationship implies that a more comprehensive empirical approach and more substantial data set are required to examine and analyze the impacts of GTI, GEN and FDI on envrionmental degradation, this becomes one of the primary research concerns of our study. Thus, through a comprehensive examination of these complex variables, policymakers would possess the essential understanding to make well-informed choices that raise both sustainable development and the conservation of Pakistan’s vulnerable and fast-declining natural environment.

The organization of this paper is as follows: the second section comprises a “Literature Review” of pertinent scholarly works. The third section summarizes the methods, econometric models, and data used. The fourth section depicts the empirical findings and the last section presents the conclusion, policy implications, and limitations of the study based on the current situation in Pakistan.

Empirical evidence and hypothesis development

Green technological innovation and ecological footprint (EFP)

Green technological innovation (GTI) is a unique form of advancement in technology that holds the capacity to diminish environmental harm. It is characterized by substantial investments, considerable risks, and extensive research and development processes. Based on this assertion, economies encounter substantial barriers when carrying out GTI operations. The literature commonly assesses GTI using two methods: total green factor productivity²¹, and patent authorizations. The recent exponential growth of GTI has prompted companies, higher education institutions, and governments to allocate resources toward enhancing GEN consumption and efficiency while simultaneously addressing climate change issues.²²

Prior studies have verified that GTI has emerged as a highly effective approach for attaining sustainable development, which involves the smooth integration of economic progress and ecological preservation.^23,24 GTI facilitates the improvement of manufacturing processes, necessitating the integration of distinct components employed in the production phase.²⁵ Cutcu et al.,²⁶ state that the successful attainment of goals pertaining to GTI development and adoption necessitates substantial initial investments, which may be beyond the financial capabilities of firms in poor nations. Khan et al.,²⁷ discovered that GTI provide a substantial contribution to the growth of renewable energy consumption. Thus, GTI’s primary objective is to promote sustainable energy generation and transportation, with a specific emphasis on integrating emerging technology into sustainable innovation. It is crucial to shift from fossil fuels to GEN with GTI, in order to effectively combat ecological deterioration.²⁸ Martin et al.²⁹ attribute the decline in EFPs to multiple sources, including the advancement of electric vehicles, energy-efficient public transportation, and sustainable urban planning. In their study, Raihan,³⁰ investigated the asymmetrical connection between GTI and environmental sustainability. They found that GTI effectively mitigates ecological deterioration, resulting in a favorable influence on environmental sustainability. Rafique et al.³¹ obtained a comparable outcome in their analysis of the BRICS nations. According to Xiao et al.³² and Huang et al.,³³ the implementation of GTI and the advancement of renewable and hydroelectric energy have effectively decreased China’s CO₂ levels. Kiani et al.³⁴ found that the implementation of GTI has significantly reduced environmental pollution in OECD nations. Bhutta et al.³⁵ have shown that the adoption of GTI in Pakistan improves ecological quality. Lin and Ullah,³⁶ also arrived at a similar finding. Shahzad et al.³⁷ have irrefutably proven that GTI has a positive influence on the ecologically sustainable progress in Pakistan's manufacturing sector. Drawing from these observations, the subsequent research hypothesis is formulated:

Hypothesis (H1) posits that the GTI has a mitigating impact on the ecological environment in Pakistan.

Green energy consumption and ecological footprint

The rapid utilization of nonrenewable resources leads to environmental deterioration, encouraging governments to shift toward comparatively sustainable energy sources.^38,39 As a result, to determine the ecological effect of GEN, considerable research efforts have been devoted and the results remain positive or statistically insignificant.⁴⁰ Based on existing research interpretations, it can be divided into two groups. The first category supports the idea that GEN utilization aids in mitigating environmental degradation. The second group advocates the disadvantage to the ecological environment or has minimal effects.

The first group of scholars asserts that GEN is of paramount importance in mitigating ecological degradation, given that conventional methods of energy production and processing cause substantial amounts of GHG emissions, which result in severe atmospheric and ecological damage. Multiple empirical studies have demonstrated that GEN consumption reshapes an efficient energy balance in order to reduce industrial development-related emissions and environmental risks.⁴¹ Consuming GEN involves gradually replaces the fossil fuels with sustainable and environmentally viable energy over time.⁴²) Studies by Godil et al.,⁴³ Shahzad et al.,³⁷ Pata et al.,⁴⁴ Safi et al.,⁴⁵ and Amin et al.⁴⁶ have shown the beneficial effects of GEN on the environmental quality. Specifically, the research findings by Shahzadi et al.,⁴⁷ Ansari et al.,⁴⁸ and Miao et al.⁴⁹ emphasize the correlation between the use of GEN and EFP. Similarly, Sharif et al.⁵⁰ using a bigger sample size of 74 economies concluded that increased demand for GEN improves environmental sustainability. The remarkable observation made by Baloch and Danish⁵¹ is that renewable energy contributes to environmental health in BRICS countries by decreasing their ecological impacts.

Conversely, the second group of scholars has proposed that the use of GEN has a detrimental or less significant impact on protecting the natural environment. Hasnisah et al.⁵² indicate that the utilization of GEN does not provide a substantial contribution to reducing environmental deterioration in developing nations in Asia. According to Sharma,⁵³ the research study indicates that the usage of genetically engineered organisms (GEN) is a significant factor in generating pollution of the environment in OPEC nations. Destek and Sarkodie⁵⁴ found that the implementation of GEN did not lead to an improvement in environmental quality in Africa, mostly because of the rise in economic activity in the region. Thus, the mixed and inconsistent results of earlier research regarding GEN and ecological degradation necessitates further investigation to arrive at a novel conclusion for a developing country like Pakistan. We draw the following hypotheses based on the occurrence of inconsistent findings in the literature:

Hypothesis (H2) posits that GEN consumption has a positive influence on Pakistan's ecological degradation.

Foreign direct investment and ecological footprint

As yet, scholars remain divided and uncertain regarding the FDI effect on the ecological degradation of the recipient nation. These effects have been widely defined through the two widely recognized hypotheses i.e., pollution haven (PHH) and pollution halo (HH) hypothesis. FDI's impact may result in a structural transformation in the host nation, characterized by the contraction of one sector and the expansion of another. Therefore, the ecological impacts of FDI may differ between developed and developing countries.⁵⁵

First, FDI can improve environmental quality by introducing advanced technologies and modern management skills, realizing economies of scale, and transmitting knowledge benefits from collateral effects.⁵⁶ Consequently, the advancement of technology helps to reduce environmental degradation and enhance energy efficiency. Multinational firms have the potential to create a favorable environmental influence by implementing eco-friendly production technologies and promoting environmental knowledge spillovers in host nations. The attributes indicate that FDI might serve as a viable means of financing sustainability and mitigating ecological degradation.^57,58 Wang et al.¹⁶ discovered that FDI had a positive mitigating impact on EFP in the long term. The findings indicate that a shift toward GEN consumption can effectively mitigate GHG emissions. This phenomenon is referred as the HH hypothesis.^59–62 Blanco et al.,⁶³ Udemba and Yalçıntaş,⁶⁴ and Kisswani and Zaitouni⁶⁵ have established a correlation between FDI and EFP using samples from various countries. Thus, the EFP and FDI are linked in a bidirectional causal approach indicating a beneficial impact on the environment.

The second opinion contends that host countries attract FDI by enforcing laxer environmental regulations, which subsequently lead to ecological degradation. Thus, FDI is considered the main driver of environmental deterioration, as it intensifies energy consumption and worsens environmental damage by facilitating the transfer of energy-intensive and polluting industrial processes, which leads to the PHH hypothesis.⁶⁶ Espoir et al.⁶⁷ assert that FDI has a detrimental impact on the ecosystem by deteriorating its quality, hence increasing the EFP. According to studies by Agboola et al.⁶⁸ and Hossain et al.,⁶⁹ there is a encouraging connection between FDI and ecological degradation in Turkey and Bangladesh, respectively. The research by Faheem et al.⁷⁰ regarding the connection between EFP and FDI provided proof in favor of the PHH hypothesis. The increasing significance of FDI as a means of international financing in developing countries has led to concerns about the environmental consequences of these capital flows.⁷¹ In addition, Ozturk et al.⁷² examined the symmetrical and asymmetrical impacts of FDI on the ecological condition of specific Asian nations. Foreign investors are mostly attracted to primary industrial sectors, which leads to a rise in resources consumption and worsens environmental damage. A research study conducted by Amin et al.⁴⁶ has found that the influx of FDI has led to higher levels of pollution in the E9 countries, primarily due to greater industrialization. The report also emphasizes the significance of pollution that is specific to certain sectors, particularly in industries such as textiles and manufacturing. Rahman et al.⁷³ found empirical proof that supports the PHH theory, indicating a direct association between FDI and higher levels of pollution emissions. Recently, Mahmood et al.⁷⁴ has uncovered that FDI aimed at promoting industrialization has resulted in a rise in environmental degradation within Pakistan. Manocha⁷⁵ recently disclosed that FDI in developing Asian countries leads to the establishment of PHHs. Besides, these governments are not actively seeking investment flows that prioritize environmental sustainability. Thus, based on the existing literature, we put up the subsequent research hypothesis:

Hypothesis (H3) posits that there exists a correlation between FDI and ecological degradation in Pakistan.

Data and empirical modeling

Data and variables

Data: This research dealt with the influence of GEN, GTI, and FDI on EFP while controlling GDP and TO in Pakistan. Due to data unavailability of some variables, like GTI, we utilized the yearly time-series dataset spanning from 1999 to 2022. Further details of study parameters are shown in Table 1. This study emphasizes the relevance of selecting appropriate variables to ensure reliable as well as adaptive conclusions, facilitating the smooth implementation of policies. This study systematically chose dependent and independent variables from the following aspects.

Table 1.

Variables with their description.

Variable	Proxy	Data source
EFPs	EFP constant per capita	Global footprint network
GEN	Green energy consumption of total	WDI
GTI	Environmental patents, % of total patents	OECD. Stat
FDI	FDI net (current USD)	WDI
EGR	GDP current USD	WDI
TO	ratio of total trade (exports plus imports) to (GDP)	WDI

Dependent variables

EFP: The recent criticism of using CO₂ emissions as a measure of environmental sustainability is inadequate. Similarly, in case of Pakistan, it only accounts for 37% of the total GHGs, which can yield biased results; thus, recent studies have concentrated on EFP as a robust measurement of environmental quality.^76–78 Accordingly, EFP has been adopted.

Independent variables

GEN: We opted the percentage of renewable energy sources, including biomass, hydro, thermal, wind, and solar, in Pakistan's overall energy consumption. The purpose is to evaluate the specific effect of these sources on the EFP, which is of special importance for Pakistan's energy policy shaping.

GTI: Empirical research currently use two main approaches for quantifying GTI; first, researchers have used total factor productivity (TFP)^40,58 and total green innovation patent authorization of the nation^79,80 as the primary measures of GTI. As a result, the measure of green patents is a more recent and advanced way to measure GTI; hence, we adopted it for this study.

Foreign direct investment (FDI): The current study adopted the FDI net value in US$ million as a percentage of GDP invested annually.

Control variables

Trade openness (TO): This study employs the trade-to-GDP ratio as a metric to assess TO, which encompasses both the total imports and exports of commodities and services.

Economic growth (EGR): For the sake of ensuring consistency in measuring economic development over time and reducing the influence of price fluctuations, economists use GDP per capita in constant 2015 dollars as a benchmark for EGR indicators. Given the significant relationship between FDI, GEN, and GTI, the GDP and TO were selected after conducting a unit root test.

Theoretical analysis and model development

Theoretically, Pakistan's economic policies have mainly depended on imported fossil energy supplies in order to accelerate economic growth (EGR). Nevertheless, the disposal of these fossil fuels also contribute to environmental problems. Therefore, it is crucial to attain energy self-sufficiency by utilizing renewable energy sources in order to tackle the ongoing increase in energy requirements and the resulting consequences of climate change. GEN sources, including as biomass, solar, wind, hydropower, and geothermal power, enhance energy efficiency and promote environmental health by reducing the negative impacts on the environment⁸¹). Hence, the concept of GEN is a recurring topic in research addressing ecological deterioration.²¹ Pakistan possesses the 28th largest coal reserve in the world, estimated at 185.175 billion tons, and benefits from 3000 to 3300 h of sunshine annually.⁸² By utilizing its cost-effective energy sources, Pakistan may improve energy security and mitigate environmental damage, even if it employs coal through GTI. Hence, given its current energy security and ecological degradation challenges, Pakistan is an ideal candidate for such transformation. Furthermore, Pakistan has encouraged the establishment of energy-related (GTIs) for coal, natural gas, and environmentally friendly energy sources. Green innovation theory posits that GTI has a significant impact on energy conservation through various techniques such as waste heat utilization, energy retention systems, waste management processes, and materials reclamation technologies. These measures effectively contribute to the decrease of environmental degradation. GTI promotes the adoption of more efficient and adaptable business methods that utilize cleaner processes and technology. This results in reduced environmental footprint, improved resource efficiency, increased potential for sustainable business practices, and decreased environmental deterioration.⁸³ The “pollution haven” theory (PHH), a widely recognized concept, can also be employed to evaluate the ecological impacts of FDI. This theory states that sectors that pollute a lot and use a lot of resources may move to host countries where FDI is present. This transfer may lead to a substantial increase in ecological degradation because of less strict environmental laws in the countries.^84,85 Furthermore, according to Magazzino,⁸⁶ ecological modernization theory generates apprehensions regarding the connection between economic expansion and environmental preservation. Hence, EMT offers a robust theoretical underpinning to measure ecological degradation. The research presents the following baseline models based on the above theoretical analysis: The estimation function follows the specifications of Chunling et al.⁸⁷

E F P = f (G E N, G I N, F D I, G D P, T O P)

(1)

However, to acquire the direct elastic characteristics of parameters and streamline the estimate procedure, the current study utilized the natural logarithm of the variables. The empirical logarithmic transformation (LN) form can be written as follows. In the present scenario, Equation (2) describes our fundamental model.

\begin{aligned} \ln E F P_{t} = & β_{o} + β_{1} \ln I N D_{t} + β_{2} \ln F D I_{t} + β_{3} \ln G E N_{t} + β_{4} \ln G I N_{t} \\ + β_{5} \ln G D P_{t} + β_{6} \ln T O P_{t} + ε_{i} \end{aligned}

(2)

Where “ln” is used to denote the natural logarithm., while

β

is the coefficient value, t is the time series, which covers 23 years, t = 1999–2022,

ε_{i}

is the stochastic error term. The EFP, stands for environmental degradation in this regard, is a crucial metric utilized in Pakistan to assess ecological degradation. We utilize an yearly dataset from 1999 to 2022 for Pakistan.

Econometric strategy

We apply the FMOLS and CCR models to examine the linkages between the GTI, GEN, FDI, and EFP. One of the key benefits of choosing the FMOLS method is that it addresses the issues of endogeneity and serial correlation in the OLS estimator⁸⁸.

Unit root test

By utilizing the augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests, this section attempts to evaluate the stationarity of our time-series data.^89,90 The PP test, acknowledged for its adaptability to heteroskedasticity, and the ADF test, which incorporates lagging differences into the model to detect a unit root. Both aim to determine whether the time series is stationary (according to the alternate hypothesis) or non-stationary with a unit root (in accordance to the “null” hypothesis). To reach the conclusion of stationarity, we assessed the generated test statistics in relation to critical values. By rebutting the null hypothesis, which indicated stationarity, we achieved the goal to guide our subsequent methodical choices in the analysis. Equation 3 representing the ADF test is as follows:

\begin{aligned} Δ Y_{t} = & α Y_{t - 1} + θ_{1} Δ Y_{t - 1} + θ_{2} Δ Y_{t - 2} + \dots + + θ_{k} Δ Y_{t - k} + μ_{t} \\ Δ Y_{t} = & α Y_{t - 1} + \sum_{k = 1}^{K} θ_{k} Δ Y_{t - k} + μ_{t} \end{aligned}

(3)

where, K is the number of lags or auto-regressive terms,

Δ Y_{t - 1} = Y_{t - 1} - Y_{t - 2} Δ Y_{t - 2} = Y_{t - 2} - Y_{t - 3}

and so on

\sum_{k = 1}^{K} θ_{k} Δ Y_{t - k}

are the additional auto-regressive terms. As we observed that our time series is not stationary at level, we proceeded to the next step by using the first difference of the variables. The three equations of the ADF test adjusted using the first difference form can be expressed as follows:

With trend and drift

\Rightarrow Δ^{2} Y_{t} = β_{1} + β_{2} t + α Δ Y_{t - 1} + \sum_{k = 1}^{K} θ_{k} Δ^{2} Y_{t - k} + μ_{k}

With only drift

\Rightarrow Δ^{2} Y_{t} = β_{1} + α Δ Y_{t - 1} + \sum_{k = 1}^{K} θ_{k} Δ^{2} Y_{t - k} + μ_{k}

Without trend and drift

\Rightarrow Δ^{2} Y_{t} = α Δ Y_{t - 1} + \sum_{k = 1}^{K} θ_{k} Δ^{2} Y_{t - k} + μ_{k}

(4)

The dependent variable has been transformed from

Δ Y_{t}

Δ^{2} Y_{t}

. After taking the first difference, the variable associated with the coefficient “a” has also been transformed to the first difference form, affecting all auto-regressive terms. Second, the PP test involving fitting the regression can be expressed as follows:

y_{i} = α + ρ y_{i - 1} + ϵ_{i}

(5)

Where we have the option to eliminate the constant or integrate a drift component. Both numerical statistics,

Z_{ρ}

and

Z_{τ}

are calculated as

\begin{aligned} Z_{ρ} = & n ({\hat{ρ}}_{n} - 1) - \frac{1}{2} \frac{n^{2} {\hat{σ}}_{n}^{2}}{{\hat{s}}_{n}} ({\hat{λ}}_{n}^{2} - {\hat{γ}}_{0, n}) \\ Z_{τ} = & \sqrt{\frac{{\hat{γ}}_{0, n}}{{\hat{λ}}_{n}^{2}}} \frac{({\hat{ρ}}_{n} - 1)}{\hat{σ}} - \frac{1}{2} ({\hat{λ}}_{n}^{2} - {\hat{γ}}_{0, n}) \frac{1}{{\hat{λ}}_{n}^{2}} \frac{n \hat{σ}}{{\hat{s}}_{n}} \end{aligned}

{\hat{γ}}_{j, n} = \frac{1}{n} \sum_{i = j + 1}^{n} {\hat{u}}_{i} {\hat{u}}_{i - j}

(6)

\begin{aligned} {\hat{λ}}_{n}^{2} = & {\hat{γ}}_{0, n} + 2 \sum_{j = 1}^{q} (1 - \frac{j}{q + 1}) {\hat{γ}}_{j, n} \\ s_{n}^{2} = & \frac{1}{n - k} \sum_{i = 1}^{n} {\hat{u}}_{i}^{2} \end{aligned}

where

{\hat{u}}_{i}

represents the OLS residual, k denotes the number of covariates in the regression, q is the number of Newey–West lags to use in calculating

{\hat{λ}}_{n}^{2}

, and

\hat{σ}

indicates the OLS standard error of

\hat{ρ}

Cointegration test

Following validation of data stationarity through the first difference, our research into cointegration in the time-series data proceeded by employing the Johansen cointegration test.⁹¹ The Johansen test is a highly efficient method for identifying the number of cointegrating relationships among multiple time series. Two statistical metrics that have been generated by the research are eigenvalues and trace statistics. According to the null hypothesis, there is no evidence suggesting that the variables being studied are cointegrated. On the contrary, the alternative hypothesis, posits that cointegration exists between the variables. The formal expression of Johansen's procedure for implementing vector autoregression (VAR) is as follows:

x_{t} = μ + A_{1} x_{t - 1} + \dots + A_{p} x_{t - p} + w_{t}

(7)

The vector-valued mean of the series is denoted by μ. The coefficient matrices for each lag are represented by A_i, and the multivariate Gaussian noise term with mean zero is denoted by w_t. At this level, we can construct a vector error correction model (VECM) by taking the difference of the series.

Δ x_{t} = μ + A x_{t - 1} + Γ_{1} Δ x_{t - 1} + \dots + Γ_{p} Δ x_{t - p} + w_{t}

(8)

The differencing operator Δx_t is defined as the difference between x_t and x_(t-1). A represents the coefficient matrix for the first lag, whereas Γ_1 represents the matrices for each differenced lag. The trace test and maximum eigenvalue test can be phrased as follows:

\begin{aligned} J_{trace} & = - T \sum_{i = r + 1}^{n} \ln (1 - {\hat{λ}}_{i}) \\ J_{\max} & = - T \ln (1 - {\hat{λ}}_{r + 1}) \end{aligned}

(9)

The sample size is represented by the variable T, and the ith largest canonical correlation is represented by the variable λ ^_i^. The trace test examines the null hypothesis of r cointegrating vectors by comparison with the alternative hypothesis of n cointegrating vectors. In the maximum eigenvalue test, the null hypothesis of r cointegrating vectors is compared with the alternative hypothesis of r + 1 cointegrating vectors.

Fully modified OLS and CCR

In this study, we employ the fully modified ordinary least-squares (FMOLS) model developed by Pedroni⁹² and the canonical cointegration regression (CCR) model proposed by Park to determine the long-term associations in our cointegrated time-series data. FMOLS is specifically designed to tackle the issues of serial correlation and endogeneity that frequently arise in cointegrated systems. It improves upon the least-squares estimation method to provide reliable long-term coefficient estimates.⁹³ FMOLS has exceptional characteristics in small sample sizes that allow for the adjustment of both lagging and leading variables to correct for potential bias. The FMOLS estimator takes into account nuisance parameters, possible autocorrelation of residues, and heteroskedasticity occurrences. Furthermore, it eliminates the potential problem of endogeneity associated with the explanatory factors. Like FMOLS, CCR is a statistical technique that is used to evaluate if cointegration patterns exist in a time series that exhibits integration of order one (1). This emphasis that FMOLS takes on both data and variable transition is the primary difference between the CCR and FMOLS approximation methods.⁸⁸ Time-series analysis frequently faces the challenges of simultaneity and endogeneity in cointegrated relationships; CCR is especially adept at addressing these concerns. By implementing a framework that accounts for both serial correlation and endogeneity biases, this method improves the robustness of our estimates. Through the integration of CCR, we assured that our analysis adequately considered any possible distortions in the identified long-term relationships. In particular, the validation of the consistency and dependability of our findings was facilitated by the implementation of CCR, which bolstered confidence in the FMOLS estimation outcomes.

Summary of analytical process

The methodology summary explains that the statistical procedure for analyzing time-series data, as shown in Figure 2 (the flowchart of methodology), started with a unit root test (ADF and P-P) to ascertain the stationarity of the data. We conducted a cointegration test using the Johansen and ADF methods to inspect whether there are long-term equilibrium linkages between variables, assuming that the data is stationary. We performed a regression analysis using the FMOLS method on the cointegrated data. Subsequently, we conducted robustness confirms using the CCR technique. The procedure involved multiple diagnostic inspections to verify the accuracy of the regression model prior to ending the analysis.

Empirical results and discussion

Descriptive statistics

Table 2 aims to offer a thorough outline of data by revealing the essential parameters of a dataset, such as mean, max, min, S.D., etc., as displayed in Table 2. EFP also recorded mean and standard value of 1.48 and 1.63, correspondingly. The mean and standard deviation of GEN are 47.24 and 2.52, respectively. Also, the GTI shows a mean value and standard deviation of 1038.21 and 339.93, respectively, while the FDI values are 1.11 and 0.97, respectively. Moreover, the mean and standard deviation of GDP are 992.08 and 383.10, respectively. Given that all variables have values larger than 1, it may be concluded that they all follow a platykurtic distribution. This distribution is necessary to assess whether the data is too skewed.

Table 2.

Descriptive statistics.

	Variable	Obs	Mean	SD	Min	Max
	EFP	23	1.48	1.63	1.21	1.82
	GEN	23	47.24	2.52	42.1	51.54
	GTI	23	1038.21	339.93	0	1738
FDI		23	1.11	0.94	0.376	3.668
	GDP	23	992.08	383.10	420.683	1620.743
	TO	23	29.99	3.32	24.702	35.682

Results of unit root test

Table 3 presents the results through ADF and PP tests. Once we made the first difference, the results of both showed that all variables exhibited stationarity. After verifying that the data at hand corresponds to an order I (1) scenario, we proceeded to conduct the Johansen cointegration test. According to the findings, it is evident that the unit root method indicates the non-stationarity of all components at the level. However, when we tested the variables using the initial variance, they stabilized at 1%. Given this, we can settle that all parameters are fit for econometric modeling and study due to their simultaneous integration.

Table 3.

ADF and PP unit root test results.

	Variables	ADF		PP
	Variables	Statistics	5% Critical value	Statistics	5% Critical value
Level form	lnEFP	1.147	−1.950	−1.863	−12.500
	lnGTI	−0.709	−1.950	−17.707	−12.500
	lnGEN	−0.648	−1.950	−4.350	−12.500
	lnFDI	−1.329	−1.950	−6.575	−12.500
	lnGDP	1.383	−1.950	−0.840	−12.500
	lnTOP	−0.083	−1.950	−8.348	−12.500
First difference	dEFP	−3.799	−1.950	−20.658	−12.500
	dGTI	−4.269	−1.950	−28.521	−12.500
	dGEN	−2.695	−1.950	−14.663	−12.500
	dFDI	−2.828	−1.950	−14.045	−12.500
	dGDP	−2.971	−1.950	−15.975	−12.500
	dTOP	−3.167	−1.950	−25.478	−12.500

Statistical significance levels: 1%, 5%, and 10%. The null hypothesis is rejected at the 5%. Notably, EFP, GEN, GTI, FDI, GDP, and TO.

Cointegration test results

The Johansen cointegration test⁹¹ indicates that the parameters are cointegrated and exhibit long-term relationships, suggesting the presence of at least three cointegration relationships providing strong support for the null hypothesis. The findings indicates that the variables showed long-term cointegration. The data has reached a state of stability over a period of time, attaining stationarity. This suggests that the variables demonstrate a continuous and correlated pattern over a longer period. The results indicate that EF, GEN, GIN, FDI, GDP, and TOP maintain cointegration in the long term with a significance level of 5% (Table 4).

Table 4.

Johansen's cointegration test.

Max rank	Parms	LL	Eigenvalue	Trace statistics	5% critical value
0	42	127.23333	.	249.7794	94.15
1	53	187.46588	0.99758	129.3143	68.52
2	62	220.38262	0.96281	63.4808	47.21
3	69	239.88553	0.85777	24.4750*	29.68
4	74	247.80666	0.54711	8.6328	15.41
5	77	250.9322	0.26842	2.3817	3.76
6	78	252.12304	0.11227

Note: * indicate the significant level at 10%.

Long-run estimates

To check the long-term relations and impact of chosen variables, we applied the FMOLS and CCR models.⁹³ We limited the explanation of the results to our major variables, which are lnEFP, lnGTI, lnGEN, and lnFDI in Table 5. Drawing on the findings, it is critical to realize that, GTI, TOP, and GEN usage have a statistical significance and mitigating the EFP in Pakistan. In particular, the coefficient values for GTI showed a negative sign, which means GTI development might be able to slow down the ecological degradation. This implies a correlation between an increase in GTI developments and a drop in the EFP. 1% increase in GTI levels marks in a significant reduction in EFP, with a degree of −0.118. The long-term impact of GTI on resource consumption, particularly energy resources in Pakistan, leads to a significant reduction in EFP. The results are an indication that if Pakistan prioritizes GTI developments, a move in this direction would help bring about notable environmental sustainability. This finding validates earlier research, which confirms that GTI helps reduce ecological degradation.^94,95 This also confirms hypothesis 1. Multiple empirical studies have demonstrated a remarkable trend in energy technology development, as GTI serves as a strategic approach to support EGR while simultaneously minimizing the negative repercussions of ecological degradation.⁹⁶ More importantly, according to Awosusi et al.⁹⁷ and Chunling et al.⁸⁷ in Pakistan, GTI is increasing rather than decreasing the EFP; our analysis disproves their conclusions.

Table 5.

Baseline regression.

	(1)	(2)
Variables	FMOLS	CCR
lnGTI	−0.132***	−0.131***
	(0.00422)	(0.00571)
lnGEN	−0.336***	−0.336***
	(0.0198)	(0.0242)
LnFDI	0.0512***	0.0511***
	(0.00122)	(0.00212)
lnGDP	0.184***	0.184***
	(0.00295)	(0.00199)
lnTOP	−0.0466***	−0.0470***
	(0.00507)	(0.00489)
Constant	19.92***	19.92***
	(0.0916)	(0.0845)
MeanVif	5.42
Breusch–Pagan/Cook–Weisberg	p = .6965
Durbin Watson	1.690142
Breusch–Godfrey	p = 0.7844

Observations	20	20
R ²	.967	.982

Note: Standard errors in brackets. ***p < .01.

Furthermore, GNE exhibits a negative relationship with EFP in both FMOLS and CCR. The increased use of GEN is leading to a decrease in ecological degradation in Pakistan. Earlier research^98–100 has largely validated the environmental mitigating benefits. Pakistan is experiencing a notable transition toward renewable energy (GEN) due to the government’s commitment to producing clean energy, higher investments in renewable energy initiatives, the availability of favorable renewable energy sources, and growing involvement of the private sector in the country.

Furthermore, the findings demonstrate a positive relationship between FDI, GDP, and EFP, suggesting that these factors lead to an upward trend in the EFP. All the findings demonstrate a high degree of statistical significance, with a commensurate increase of 0.04% in the EFP. Since, the heightened influx of FDI not only boosts economic initiatives but also significantly impacts the environment, transforming the country into a PHH, consistent with the results established by Ozturk et al.⁷² and Naqvi et al.¹⁰¹ The continued and insufficient utilization of natural resources in each sector of the economy exacerbates ecological degradation.¹⁰² It is important to acknowledge that our findings exhibited flexibility even when subjected to the CCR model, indicating their robustness. The CCR model exhibits a high degree of consistency, albeit with a minor fluctuation in the coefficient.

Model robustness test

We executed several diagnostic tests, in order to validate the reliability of the model. The model was chosen based on three components of the lasso model selection test: cross-validation, adaptive lasso, and BIC criteria. We assessed the robustness of the FMOLS model output using the CCR model. There is no empirical support indicating that the average variance inflation factor (VIF) is below 10, therefore indicating the lack of multicollinearity. Furthermore, the conclusion of the Breusch–Pagan/Cook–Weisberg autocorrelation test has verified the absence of autocorrelation due to the insignificance of the p-value. The results of the lasso model selection test are shown in Table 5.

Cross-validation plot

Table 6 demonstrates that the three performed components of the lasso model selection have identified the five variables as highly reliable predictors of our dependent variable. The cross-validation illustration for lasso regression demonstrates the validity of the model at different lambda values. The optimal value is 0.0028, at which the model achieves the minimum cross-validation error, proving an optimal equilibrium between bias and variance. Currently, the model retains five coefficients that are non-zero, indicating that five factors are deemed significant for predicting the result. The plot serves to guide the selection of variables, ensuring that the model does not suffer from either overfitting or underfitting, while still preserving a clear set of predictive properties (Figure 3).

Table 6.

Lasso model selection.

Variables	Cross-validation	Adaptive lasso	BIC
lnGNE	X	X	X
lnGTI	X	X	X
lnFDI	X	X	X
lngGDP	X	X	X
lnTOP	X	X	X
Constant	X	X	X

Note: “X” stands for the selected variable.

Conclusion, policy implications, and limitations

Conclusion

This research study employed various econometric methods, such as FMLOS and CCR models, to determine the relationship between GEN, GTI, and FDI on EFP from 1999 to 2022 in Pakistan. We initially assessed the data’s stability using the PP and ADF unit root tests. We then employ the Johansen co-integration method to identify integration among the variables of interest. This work employs complex estimators, such as FMOLS and CCR models, to determine long-term coefficients. The study of co-integration establishes an empirical foundation for the presence of a stable, long-lasting equilibrium association among the variables. The findings revealed that GNE, GTI, and TO have reduced the ecological degradation in the region. While FDI and GDP have a undesirable impact on Pakistan’s environmental sustainability. Based on these outcomes, the research suggests subsequent policy implications for Pakistan.

Policy implications

As the economies confronts the climate change, energy resource depletion, and ecological degradation issues, a refined and specific approach to promote sustainable and environmentally conscious development. We provide additional recommendations to accelerate green horizon in Pakistan, as there is still room for improvement.

Pakistan can significantly reduce energy poverty by utilizing its plentiful renewable energy resources, particularly wind and solar power, as well as by benefiting from multilateral development banks (MDBs), development finance institutions (DFIs), and programs like the China–Pakistan Economic Corridor (CPEC). Furthermore, it is crucial to have legislation that specifically targets the goal of increasing public awareness regarding environmental concerns and the advantages of utilizing GEN sources. Moreover, GTI has a crucial influence on reducing ecological degradation, emphasizing the importance of enacting laws that promote environmentally friendly investment and innovation in order to achieve sustainability. Allocating financial and material resources to increase the scope of GTI can aid in mitigating national energy issues and improving energy efficiency. Government funding, green financing, and technology support ought to expedite the shift toward green industrialization. It is advisable to implement policies that provide incentives for environmentally friendly ventures, such as offering loans and subsidies specifically for green businesses and consumers. Utilizing agricultural resources for the production of biodegradable plastics can aid in the reduction of pollution. It is important to provide incentives that motivate universities, multinational corporations, and domestic companies to actively participate in GTI research. Utilizing globalization, specifically through initiatives such as CPEC, can expedite the execution and evaluation of cutting-edge GTI and green energy production. In order to address environmental degradation, it is imperative to augment the proportion of GEN, despite the significant contribution of FDI in promoting EGR and technological progress. Promoting more sustainable FDI practices, as well as advancing GTI, is crucial for effectively mitigating ecological degradation.

Limitations and future research

An important limitation is the neglect of considering the impact of environmental regulations due to various reasons. Moreover, the scope of this research is limited to the Pakistani context. Further research is recommended to expand the choice of future research to encompass multiple global regions in order to enhance its applicability. In order to enhance the overall comprehension of ecological degradation, subsequent investigations may incorporate supplementary variables that were not accounted for in the current study. These variables might comprise environmental regulations, the depletion of natural reserves, economic policy uncertainty, green human capital development, and the implementation of green governments. In future, heterogenous and spatial analysis could also be conducted. As a result, geographical locations (e.g., cities and provinces) or components (e.g., energy sources) can be evaluated. Consequently, a greater degree of comprehensiveness can be attained. With the exception of data unavailability concerns, the study concludes by examining the temporal span from 1999 to 2021. As a result, we advise that prospective research endeavors employ contemporary methodologies, including QQR regression, and sector-specific data in order to examine subsequent periods with readily available information. Notwithstanding these constraints, the research has achieved its objectives.

Footnotes

Authors’ contributions

AAR served as lead for conceptualization, methodology, formal analysis, software, and writing–original draft. MF served as lead for review of literature, methodology, validation, and writing–review and editing. AN served as lead for supervision, validation, project administration, and writing–review and editing. RSS served as lead for data curation, funding, and writing–review and editing. MR served as lead for investigation and writing–review and editing.

Data availability

All data generated or analyzed during this study are publicly available and sources are included in the published article.

Declaration of conflicting interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. Researchers Supporting Project number (RSP2025R87), King Saud University, Riyadh, Saudi Arabia.

ORCID iD

Magdalena Radulescu

Correction (November 2024):

This article has been updated with corrections in the section ‘Funding’. For more details, please see the correction notice https://journals.sagepub.com/doi/10.1177/0958305X241299965.

Abid Ali Randhawa, PhD scholar majoring applied economics, at Business School, Nanjing Normal University, P.R. China has received his BSc(Hons) and MSc(Hons) in Agricultural Economics from Sindh Agriculture University and worked seven years in the same institution as Lecturer of Agribusiness Management. He has published 7 research papers in the field of Agricultural economics and applied economics.

Mahamane Famanta is PhD scholar majoring Applied Economics at Business school, Nanjing Normal University. He has published five research paper in Development Economics, Welfare Economics, and Socioeconomics.

Abdelmohsen A. Nassani is a Full Professor at King Saud University, College of Business Administration, Saudi Arabia. He obtained his bachelor's degree from the University of Aleppo, Syria. He obtained his master's and PhD degrees in management sciences from Ain Shames University, Egypt. He is the author of more than 60 publications in different peer–reviewed research journals. His work has been published in the Journal of Cleaner Production, Environmental Science and Pollution Research, Resources Policy, Clean Technology and Environmental Policy, Sustainability, Polish Journal of Environmental Studies, Air Quality, Atmosphere and Health, Ocean and Coastal Management, Environmental Research, Environmental Progress & Sustainable Energy, Journal of Policy Modeling, etc. He supervises various scholars at master's and PhD level.

Magdalena Radulescu received his BA and MA in Finance, and PhD degrees and Habilitation for PhD Supervision in Economics from the Academy of Economic Studies, Bucharest, Romania. She is Full Professor at the Department of Finance, Accounting and Economics, Faculty of Economic Studies, National University of Science and Technology Politehnica Bucharest, Romania. She is PhD Supervisor in Economics at the University “Lucian Blaga” of Sibiu, Romania. She authored and co–authored more than 130 papers in WOS journals. H–index 30 in WOS. Magdalena Radulescu serves as Topic Editor for Energies, and Guest Editor for Journal of Risk and Financial Management, Frontiers in Environmental Sciences, Frontiers in Energy Research, Environmental Science and Pollution Research, Environmental Research Communication, Utilities Policy, Environment, Development and Sustainability, Journal of Urban Technology, Resources Policy, Energy Exploration & Exploitation, Energy Economics, Geological Journal. She is in Reviewer Editorial Board of Frontiers in Environmental Science and Topic Advisory Board of Energies journal (MDPI). She is associate editor at Environment, Development and Sustainability (Springer), Helyion (Elsevier) and Geological Journal (Wiley).

References

Gołasa

Wysokiński

Bieńkowska-Gołasa

, et al. Sources of greenhouse gas emissions in agriculture, with particular emphasis on emissions from energy used. Energies 2021; 14: 3784.

Nguyen

Grote

Neubacher

, et al. Security risks from climate change and environmental degradation: implications for sustainable land use transformation in the Global South. Curr Opin Environ Sustain 2023; 63: 101322.

Ali

Shayanmehr

Radmehr

, et al.

Exploring the impact of economic growth on environmental pollution in South American countries: how does renewable energy and globalization matter?

Environ Sci Pollut Res2023; 30: 15505–15522.

Wang

. The impact of energy efficiency on carbon emissions: evidence from the transportation sector in Chinese 30 provinces. Sustain Cities Soc 2022; 82: 103880.

IAEA (2023) Country Nuclear Power Profiles (Pakistan). https://www-pub.iaea.org/MTCD/Publications/PDF/CNPP-2021/countryprofiles/Pakistan/Pakistan.htm#:∼:text=During%20the%20past%20two%20decades%2C%20the%20energy%20consumption,doubled%2C%20at%20an%20average%20growth%20rate%20of%204.4%25.

Shahbaz

Siddiqui

, et al. Exploring the growth of sustainable energy technologies: a review. Sustain Energy Technol Assess 2023; 57: 103157.

Murshed

Ozturk

. Rethinking energy poverty reduction through improving electricity accessibility: a regional analysis on selected African nations. Energy 2023; 267: 126547.

Aydin

Degirmenci

. The role of greenfield investment and investment freedom on environmental quality: testing the EKC hypothesis for EU countries. Int J of Sustain DevWorld Ecol 2024; 31: 1–11.

Zhong

Yan

, et al. How does energy utilization affect rural sustainability development in traditional villages? Re-examination from the coupling coordination degree of atmosphere-ecology-socioeconomics system. Build Environ 2024; 257: 111541.

10.

Chishti

Dogan

. Analyzing the determinants of renewable energy: the moderating role of technology and macroeconomic uncertainty. Energy & Environment 2024; 35: 874–903.

11.

Ahakwa

. Towards land degradation neutrality: does green energy and green human capital matter? Renewable Sustain Energy Rev 2024; 197: 114396.

12.

Wang

Zhang

. Revisiting the environmental kuznets curve hypothesis in 208 counties: the roles of trade openness, human capital, renewable energy and natural resource rent. Environ Res 2023; 216: 114637.

13.

Son

Kim

. Sector coupling and migration towards carbon-neutral power systems. Energies 2023; 16: 1897.

14.

IEA

. World energy outlook 2022. Paris, France: International Energy Agency (IEA), 2022.

15.

Wang

. Could information and communication technology (ICT) reduce carbon emissions? The role of trade openness and financial development. Telecomm Policy 2024; 48: 102699.

16.

Wang

Zhang

. Free trade and carbon emissions revisited: the asymmetric impacts of trade diversification and trade openness. Sustain Dev 2024; 32: 876–901.

17.

Eckstein

Kreft

. Global climate risk index 2021. Who suffers most from extreme weather events? Think Tank & Research, 2020.

18.

Ullah

Yin

, et al. Influence of large-scale circulation and interannual modes of climate variability on seasonal drought characteristics over Pakistan. 2021.

19.

Asghar

Amjad

Rehman

, et al. Causes of higher ecological footprint in Pakistan: does energy consumption contribute? Evidence from the non-linear ARDL model. Sustainability 2023; 15: 3013.

20.

Raza

Aman

Abbas

, et al. Managing the low carbon transition pathways through solid waste electricity. Sci Rep 2024; 14: 5490.

21.

Zhao

Zhang

Ali

, et al. An empirical investigation of the impact of renewable and non-renewable energy consumption and economic growth on climate change, evidence from emerging Asian countries. Front in Environ Sci 2023; 10: 75041–75057.

22.

Xin

Sun

Xia

. Renewable energy technology innovation and inclusive low-carbon development from the perspective of spatiotemporal consistency. Environ Sci Pollut Res 2023; 30: 20490–20513.

23.

Colombelli

Ghisetti

Quatraro

. Green technologies and firms’ market value: a micro-econometric analysis of European firms. Ind Corporate Change 2020; 29: 855–875.

24.

Wang

Umar

Akram

, et al. Is technological innovation making world “Greener”? An evidence from changing growth story of China. Technol Forecast Soc Change 2021; 165: 120516.

25.

Chen

Zhang

. How do property rights affect corporate ESG performance? The moderating effect of green innovation efficiency. Finance Res Lett 2024; 105476: 1544–6123.

26.

Cutcu

Cil

Karis

, et al. Determining the green technology innovation accelator and natural resources towards decarbonization for the EU countries: evidence from MMQR. Environ Sci Pollut Res 2024; 31: 1–20.

27.

Khan

SAR

Ponce

Thomas

, et al. Digital technologies, circular economy practices and environmental policies in the era of COVID-19. Sustainability 2021; 13: 12790.

28.

Sovacool

Griffiths

Kim

, et al. Climate change and industrial F-gases: a critical and systematic review of developments, sociotechnical systems and policy options for reducing synthetic greenhouse gas emissions. Renewable Sustain Energy Rev 2021; 141: 110759.

29.

Martin

Heiska

Björklund

. Environmental assessment of a product-service system for renting electric-powered tools. J Cleaner Prod 2021; 281: 125245.

30.

Raihan

. Green energy and technological innovation towards a low-carbon economy in Bangladesh. Green and Low-Carbon Economy 2023: 1–11.

31.

Rafique

Larik

, et al. The effects of FDI, technological innovation, and financial development on CO₂ emissions: evidence from the BRICS countries. Environ Sci Pollut Res 2020; 27: 23899–23913.

32.

Xiao

Chen

Han

, et al. The carbon emission reduction effect of renewable resource utilization: from the perspective of green innovation. Atmos Pollut Res 2024; 15: 102121.

33.

Huang

Wang

Jiang

, et al. Environmental policy uncertainty and corporate green innovation: evidence from China. Eur J Innov Manage 2023; 26: 1675–1696.

34.

Kiani Mavi

Farzipoor Saen

, et al. Eco-innovation analysis of OECD countries with common weight analysis in data envelopment analysis. Supply Chain Manage: An Int J 2022; 27: 162–181.

35.

Bhutta

Ullah

Sultan

, et al. Impact of green energy production, green innovation, financial development on environment quality: a role of country governance in Pakistan. Int J of Energy Econ Policy 2022 12: 316–326.

36.

Lin

Ullah

. Towards the goal of going green: do green growth and innovation matter for environmental sustainability in Pakistan. Energy 2023; 285: 129263.

37.

Shahzad

Radulescu

Rahim

, et al. Do environment-related policy instruments and technologies facilitate renewable energy generation? Exploring the contextual evidence from developed economies. Energies 2021; 14: 690.

38.

Majeed

Tauqir

Mazhar

, et al. Asymmetric effects of energy consumption and economic growth on ecological footprint: new evidence from Pakistan. Environ Sci Pollut Res 2023; 28: 32945–32961.

39.

Fang

Xiao

, et al. Drivers of renewable energy penetration and its role in power sector's deep decarbonization towards carbon peak. Renewable Sustain Energy Rev 2023; 178: 113247.

40.

Wang

Guo

. Revisiting the environmental Kuznets curve (EKC) hypothesis of carbon emissions: exploring the impact of geopolitical risks, natural resource rents, corrupt governance, and energy intensity. J Environ Manage 2024; 351: 119663.

41.

Behera

Haldar

Sethi

. Achieving carbon neutrality target in the emerging economies: role of renewable energy and green technology. Gondwana Res 2023; 121: 16–32.

42.

Weng

Zhao

, et al. Investigating spatio-temporal characteristics and influencing factors for green energy consumption in China. Geosci Front 2024; 15: 101672.

43.

Godil

Sharif

, et al. Investigate the role of technology innovation and renewable energy in reducing transport sector CO₂ emission in China: a path toward sustainable development. Sustain Dev 2021; 29: 694–707.

44.

Pata

Wang

Kartal

, et al. The role of disaggregated renewable energy consumption on income and load capacity factor: a novel inclusive sustainable growth approach. Geosci Front 2024; 15: 101693.

45.

Safi

Wei

Sansaloni

, et al. Breaking down the complexity of sustainable development: a focus on resources, economic complexity, and innovation. Resources Policy 2023; 83: 103746.

46.

Amin

Wang

Shah

, et al. Exploring the dynamic nexus between renewable energy, poverty alleviation, and environmental pollution: fresh evidence from E-9 countries. Environ Sci Pollut Res 2023; 30: 25773–25791.

47.

Shahzadi

Yaseen

Khan

MTI

, et al. The nexus between research and development, renewable energy and environmental quality: evidence from developed and developing countries. Renew Energy 2022; 190: 1089–1099.

48.

Ansari

Haider

Masood

. Do renewable energy and globalization enhance ecological footprint: an analysis of top renewable energy countries? Environ Sci Pollut Res 2021; 28: 6719–6732.

49.

Miao

Razzaq

Adebayo

, et al.

Do renewable energy consumption and financial globalisation contribute to ecological sustainability in newly industrialized countries?

Renew Energy 2022; 187: 688–697.

50.

Sharif

Baris-Tuzemen

Uzuner

, et al. Revisiting the role of renewable and non-renewable energy consumption on Turkey’s ecological footprint: evidence from Quantile ARDL approach. Sustain Cities Soc 2020; 57: 102138.

51.

Baloch

Danish. The nexus between renewable energy, income inequality, and consumption-based CO₂ emissions: an empirical investigation. Sustain Dev 2022; 30: 1268–1277.

52.

Hasnisah

Azlina

Taib

CMIC

. The impact of renewable energy consumption on carbon dioxide emissions: empirical evidence from developing countries in Asia. Int J Energy Econ Policy 2019; 9: 135.

53.

Sharma

. We do not want ‘fake energy’: the social shaping of a solar micro-grid in rural India. Sci, Technol Soc 2020; 25: 308–324.

54.

Destek

Sarkodie

. Investigation of environmental Kuznets curve for ecological footprint: the role of energy and financial development. Sci Total Environ 2019; 650: 2483–2489.

55.

Saqib

Ozturk

Usman

, et al. Pollution haven or halo? How European countries leverage FDI, energy, and human capital to alleviate their ecological footprint. Gondwana Res 2023; 116: 136–148.

56.

Doytch

Ashraf

. The ecological footprints of greenfield FDI and cross-border M&A sales. Environ Model Assess 2022; 27: 935–951.

57.

Djellouli

Abdelli

Elheddad

, et al. The effects of non-renewable energy, renewable energy, economic growth, and foreign direct investment on the sustainability of African countries. Renew Energy 2022; 183: 676–686.

58.

Zhang

Niu

, et al. Looking for ecological sustainability: a dynamic evaluation and prediction on the ecological environment of the belt and road region. Sustain Prod Consump 2022; 32: 851–862.

59.

Walter

Ugelow

. Environmental policies in developing countries. Ambio 1979; 8: 102–109.

60.

Pethig

. Pollution, welfare, and environmental policy in the theory of comparative advantage. J Environ Econ Manage 1976; 2: 160–169.

61.

Omri

Hadj

. Foreign investment and air pollution: do good governance and technological innovation matter? Environ Res 2020; 185: 109469.

62.

Yilanci

Cutcu

Cayir

, et al. Pollution haven or pollution halo in the fishing footprint: evidence from Indonesia. Mar Pollut Bull 2023; 188: 114626.

63.

Blanco

Gonzalez

Ruiz

. The impact of FDI on CO₂ emissions in Latin America. Oxf Dev Stud 2013; 41: 104–121.

64.

Udemba

Yalçıntaş

. Economic and environmental implications of resource rent: a dual analysis of Venezuela's sustainability. Int Soc Sci J 2023; 73: 215–231.

65.

Kisswani

Zaitouni

. Does FDI affect environmental degradation? Examining pollution haven and pollution halo hypotheses using ARDL modelling. J Asia Pac Econ 2023; 28: 1406–1432.

66.

Qamruzzaman

. Nexus between environmental quality, institutional quality and trade openness through the channel of FDI: an application of common correlated effects estimation (CCEE), NARDL, and asymmetry causality. Environ Sci Pollut Res 2021; 28: 52475–52498.

67.

Espoir

Sunge

Nchofoung

, et al. Analysing the drivers of ecological footprint in Africa with machine learning algorithm. Environ Impact Assess Rev 2024; 104: 107332.

68.

Agboola

Hossain

Gyamfi

, et al. Environmental consequences of foreign direct investment influx and conventional energy consumption: evidence from dynamic ARDL simulation for Turkey. Environ Sci Pollut Res 2022; 29: 53584–53597.

69.

Hossain

Kumar Roy

Akter

. Dynamic effects of economic growth, foreign direct investment, and trade openness on environmental quality: evidence from Asian economies. Croatian Eco Surv 2023; 25: 79–114.

70.

Faheem

Chaudhry

Farooq

, et al. Visiting human capital-foreign direct investment-environment association for attaining environmental sustainability: fresh insight from Pakistan. Rev Econ Dev Stud 2021; 7: 515–523.

71.

Xie

Zhang

. Re-examining the impact of global foreign direct investment (FDI) inflows on haze pollution—considering the moderating mechanism of environmental regulation. Energy Environ 2023: 0958305X231164679. DOI: https://doi.org/10.1177/0958305X231164679

72.

Ozturk

Farooq

Majeed

, et al. An empirical investigation of financial development and ecological footprint in south Asia: bridging the EKC and pollution haven hypotheses. Geosci Front 2023; 15: 101588.

73.

Rahman

Khattak

Ahmad

, et al. A disaggregated-level analysis of the relationship among energy production, energy consumption and economic growth: evidence from China. Energy 2020; 194: 116836.

74.

Mahmood

Mubarik

. Balancing innovation and exploitation in the fourth industrial revolution: role of intellectual capital and technology absorptive capacity. Technol Forecast Soc Change 2020; 160: 120248.

75.

Manocha

. Do FDI flows lead to environmental degradation in developing economies? A case study of select Asian economies. Vision 2024; 28: 237–250.

76.

Nathaniel

Adeleye

. Environmental preservation amidst carbon emissions, energy consumption, and urbanization in selected African countries: implication for sustainability. J Cleaner Prod 2021; 285: 125409.

77.

Idroes

Hardi

Rahman

, et al. The dynamic impact of non-renewable and renewable energy on carbon dioxide emissions and ecological footprint in Indonesia. Carbon Res 2024; 3: 1–21.

78.

Qing

Usman

Radulescu

, et al. Towards the vision of going green in South Asian region: the role of technological innovations, renewable energy and natural resources in ecological footprint during globalization mode. Resour Policy 2024; 88: 104506.

79.

Song

, et al. Accelerated green patent examination and innovation benefits: an analysis of private economic value and public environmental benefits. Technol Forecast Soc Change 2024; 200: 123105.

80.

Abbas

Saqib

Mohammed

, et al. Pathways towards carbon neutrality in low carbon cities: the role of green patents, R&D and energy use for carbon emissions. Technol Forecast Soc Change 2024; 200: 123109.

81.

Zahedi

Yousefi

Aslani

, et al. Water, energy and environment nexus: quantitative assessment for integrated power plants with renewable energy. Energy Strat Rev 2024; 53: 101410.

82.

Lin

Raza

. Analysis of energy related CO₂ emissions in Pakistan. J Cleaner Prod 2019; 219: 981–993.

83.

Meirun

Mihardjo

Haseeb

, et al. The dynamics effect of green technology innovation on economic growth and CO₂ emission in Singapore: new evidence from bootstrap ARDL approach. Environ Sci Pollut Res 2021; 28: 4184–4194.

84.

Savona

Ciarli

. Structural changes and sustainability. A selected review of the empirical evidence. Ecol Econ 2019; 159: 244–260.

85.

Stef

Ben Jabeur

. Climate change legislations and environmental degradation. Environ Res Econ 2020; 77: 839–868.

86.

Magazzino

. Ecological footprint, electricity consumption, and economic growth in China: geopolitical risk and natural resources governance. Empir Econ 2023; 66: 1–25.

87.

Chunling

Memon

Thanh

, et al. The impact of public-private partnership investment in energy and technological innovation on ecological footprint: the case of Pakistan. Sustainability 2021; 13: 10085.

88.

Zahid

Rahman

Khan

, et al. Addressing endogeneity by proposing novel instrumental variables in the nexus of sustainability reporting and firm financial performance: a step-by-step procedure for non-experts. Bus Strat Environ 2020; 29: 3086–3103.

89.

Dickey

Fuller

. Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 1979; 74: 427–431.

90.

Phillips

Perron

. Testing for a unit root in time series regression. Biometrika 1988; 75: 335–346.

91.

Johansen

. Statistical analysis of cointegration vectors. J Econ Dyn Control 1988; 12: 231–254.

92.

Pedroni

. Fully modified OLS for heterogeneous cointegrated panels. Adv in Econ 2000; 15: 93–130.

93.

Phillips

Hansen

. Statistical inference in instrumental variables regression with I (1) processes. Rev Econ Stud 1990; 57: 99–125.

94.

Razzaq

Ajaz

, et al. Investigating the asymmetric linkages between infrastructure development, green innovation, and consumption-based material footprint: novel empirical estimations from highly resource-consuming economies. Resour Policy 2021; 74: 102302.

95.

Zhang

, et al. Demand for green finance: resolving financing constraints on green innovation in China. Energy Policy 2021; 153: 112255.

96.

Liu

Diao

. Assessing regional differences in green innovation efficiency of industrial enterprises in China. Int J Environ Res Public Health 2019; 16: 940.

97.

Awosusi

Adebayo

Kirikkaleli

, et al. Role of technological innovation and globalization in BRICS economies: policy towards environmental sustainability. Int J of Sustain Dev World Ecol 2022; 29: 593–610.

98.

Gayen

Chatterjee

Roy

. A review on environmental impacts of renewable energy for sustainable development. Int J Environ Sci Technol 2024; 21: 5285–5310.

99.

Zhu

Zafar

Usman

, et al. Internalizing negative environmental externalities through environmental technologies: the contribution of renewable energy in OECD countries. Sustain Energy Technol Assess 2024; 64: 103726.

100.

Yousaf

Ali

Aziz

, et al. What causes environmental degradation in Pakistan? Embossing the role of fossil fuel energy consumption in the view of ecological footprint. Environ Sci Pollut Res 2022; 29: 1–11.

101.

Naqvi

SAA

Hussain

, et al. Environmental sustainability and biomass energy consumption through the lens of pollution haven hypothesis and renewable energy-environmental kuznets curve. Renew Energy 2023; 212: 621–631.

102.

Wang

Zhang

, et al. Does artificial intelligence facilitate the energy transition and curb carbon emissions? The role of trade openness. J Cleaner Prod 2024; 447: 141298.