Mediating macroeconomic performance in green energy policies and foreign direct investment nexus: Do transmission mechanism heterogeneities on clean energy poverty and carbon neutrality matter?

Green energy climate policies and FDI

While there is a limited amount of research on the relationship between green energy climate policies and FDI, the studies in this corpus do not provide the mechanisms through which these variables are linked. For instance, some studies have proven the existence of bidirectional causality between renewable energy utilization and FDI within South Africa. ²² Similarly, Wen et al. ¹² revealed that renewable energy consumption and foreign capital flows have bidirectional causality in South Africa and several other countries in Asia. Additionally, Abbas et al. ³⁰ found that green energy innovations enhance FDI in China, Russia, India, Brazil, and South Africa. Leitão et al. ³¹ indicated that renewable energy consumption increases FDI in Visegrad countries, and Pan et al. ²¹ found that carbon finance policies attract FDI in China. Zhang and Kong ²⁰ indicated that GEP enhance outward FDI at the firm level in China. Bidirectional causality between energy efficiency and FDI was confirmed by Panait et al. ¹³ in Europe. Although FDI as per cent of GDP in high-income nations is still declining, nothing is known about how GEP affects FDI as per cent of GDP. The additional gap in research is examining the mechanisms by which GEP functions in relation to FDI, particularly in relation to CEP and carbon neutrality. This study aims to expand on this area by examining data on high-income nations from 1995 to 2022, highlighting the need to consider pathways when linking GEP and FDI. From these findings, we formulate the link between GEP and FDI as the first hypothesis.

H1: Green energy policies enhances FDI.

Relevant mechanisms for GEP and FDI

Green energy climate policies, FDI, carbon neutrality, and CEP

In previous studies, where carbon neutrality was expressed as CO₂ emissions, many scholars showed that the adoption of renewable energies promotes carbon neutrality (^30,34,66,67 in five BRICS countries; Wen et al. ⁶⁸ in five South Asian countries including Bangladesh, India, Pakistan, Sri Lanka, and Nepal; and ^69,70). Energy efficiency was also found to have a good impact on carbon neutrality.^{47,48,71–73} Another side, recent studies have looked at the link between energy poverty and energy-related climate policies, and some results point to a reduction in energy poverty as a result of these policies.^74–77

Concerning FDI, many previous studies have demonstrated that FDI increases CO₂.^78–81 Also, several studies have pointed towards how FDI lessens poverty in energy.^82–86 After examining the previous literature, it is clear that the studies did not compare the impact of GEP and FDI on carbon neutrality and CEP alleviation, see the summary of previous literature in Table 1. Hence, there is a need to fill this gap in the literature in order to provide an effective solution to the twin energy problems of carbon emissions and energy poverty. From the above findings, we formulate the link between GEP, carbon neutrality, and CEP as the third hypothesis. Again, we formulate the link between FDI, carbon neutrality, and CEP as the fourth hypothesis.

H3: Green energy policies enhance carbon neutrality and clean energy poverty alleviation.

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Table 1.

Summary of empirical studies on the nexus among green energy policies, FDI, energy poverty and carbon neutrality.

Authors	Country	Variables	Period	Methods	Findings
Green energy policies and FDI
Ekwueme et al. 22	South Africa	Renewable energy utilization and FDI	1970–2014	VAR, ARDL, and Granger causality	Bidirectional causality between the two variables
Wen et al. 12	17 developing countries	Renewable energy consumption and foreign capital flows	1990–2020	CUP-FM, CUP-BC, and panel causality	Bidirectional causality between the two variables
Abbas et al. 30	5 BRICS	Renewable energy development and FDI	1990–2020	Panel NARDL, MG, and PMG	Positive correlation between the two variables
Leitão et al. 31	4 Visegrad countries	Renewable energy consumption and FDI	1990–2018	PMG and FMOLS	Renewable energy consumption enhances FDI
Zhang and Kong 20	China	New green energy policy and outward FDI	2007–18	PSM and DID	New green energy policy enhances outward FDI
Panait et al. 13	37 European countries	Energy efficiency and FDI	1990–2018	Granger causality	Bidirectional causality between the two variables
Macroeconomic indicators as mechanisms in connecting green energy policies and FDI
Lee et al. 32	Global level	Decreased dependence on oil price and economic stability	2004Q1–2020Q4	QQ and wavelet	Decreased dependence on oil price rises and economic stability
Baz et al. 34	Pakistan	Energy consumption and economic growth	1971–2014	NARDL	Existence of asymmetric cointegration between the two variables
Gakuru et al. 29	65 countries	Renewable energy promotion, energy efficiency, and economic growth	1995–2022	CS-ARDL and panel quantile regression	Both renewable energy promotion and energy efficiency rise economic growth
Alexandri et al. 23	Europe and global level	Electricity generation towards economic growth and employment	2015–23	Macroeconomic model with three sectors	Positive impact of electricity generation on both economic growth and employment
Crichton et al. 35	Austria	Renewable energy usage and economic output	1990–2015	Synthetic control method	Renewable energy sources increase economic output
Alshamsi and Azam 36	UAE	GDP and FDI	1980–2013	ARDL	GDP positively influences FDI
Encinas-Ferrer and Villegas-Zermeño 37	China	GDP and FDI	1995–2012	Toda and Yamamoto's technique	GDP increases FDI
Sijabat 38	ASEAN	GDP and FDI	2009–20	Panel causality	Bilateral association
Talwar and Srivastava 15	United Kingdom, United States, Brazil, Ethiopia, India, and Bhutan	GDP and FDI	2000–16	Johansen cointegration	Long-run association among the variables
Dogan et al. 40	South Asian economies	Renewable energy and job opportunities	1990–2017	SYS GMM, PQR, PMG, FMOLS, and Granger causality	Renewable energy boosts job opportunities
Cui et al. 41	China	Green finance and employment	2004–19	GMM, OLS, FGLS, and PCSE	Green finance increases employment opportunities
Liu et al. 42	China	Sustainable industrial technologies and unemployment rates	1999–2018	Machine learning	Sustainable industrial technologies help reduce unemployment rates
Saboori et al. 43	51 United States	Renewable energy consumption and employment	1977–2017	SURE model	Renewable energy consumption contributes to employment in 6 states
Arvanitopoulos and Agnolucci 44	United Kingdom	Electricity and employment creation	1990 onwards	CGE	Electricity contributes to employment creation
Gakuru et al. 33	EAC and China	Green energy inclusion policy and economic growth	1990–2022	ARDL and NARDL model	Green energy inclusion policy enhances economic growth
Khobai et al. 45	South Africa	Renewable energy consumption and job opportunities	1990–2014	ARDL	Renewable energy consumption increases job opportunities in long-run
Proença and Fortes 46	28 member countries of European Union	Renewables and jobs creation	2000–16	Pooled OLS	Positive nexus between the two
Chen et al.47,48	China	Energy efficiency and job creation	2006–19	Panel fixed-effects model	Energy efficiency contributes to jobs creation
Cantore et al. 50	Africa	Renewable energy usage and energy efficiency on employment creation	2009 onwards	Sensitivity analysis	Both renewable energy usage and energy efficiency contributes to employment creation
Hartwig et al. 49	German	Energy efficiency and macroeconomic benefits	2015 onwards	Input–output model	Energy efficiency rises both GDP and employment
Oliveira et al. 52	Portugal	Energy efficiency and employment	1946–90	Input–output model	Energy savings enhance net employment generation
Kuckshinrichs et al. 53	German	Energy efficiency and employment	2005–07	Input–output model	Energy efficiency leads to employment creation
Mirasgedis et al. 54	Greek	Energy efficiency and job opportunities	Before 1980	Input–output model	Energy efficiency has significantly boosted job opportunities
Lehr et al. 55	Tunisia	Renewable energy and energy efficiency on employment	2010 onwards	Input–output model	Positive correlation between energy efficiency, renewable energy, and job creation
Xiong and Wan 56	China	Employment opportunities and FDI	2006–11	DID method	Employment opportunities play a significant role in attracting FDI through trade
Yu and Li 57	China	Trading policy of carbon emissions and employment	2008–17	Triple difference model	Policy enhances employment
Akan 59	United States	Renewable energy, inflation, and interest rate	1980–2022	Symmetric and asymmetric analysis	Renewable energy reduces inflation and interest rate
Dinçer et al. 60	Turkey	Renewable energy and inflation	1990–2020	Engle Granger cointegration	Renewable energy decreases inflation
Ajide and Ibrahim 87	53 African countries	Determinants of FDI	1984–2018	Bayesian model averaging approach	Inflation and exchange rate are FDI determinants
Coskun 16	Turkey	Determinants of FDI	2001	Survey approach	Inflation rate is a determinant of FDI
Trevino and Grosse 63	United States	Resources and FDI	1982–96	Multivariate regression	Resources of firms attract FDI
Bai et al. 58	China	Employment and trade	2000–14	Input–output methods	Employment attracts trade
Deka et al. 19	Brazil	Renewable energy use, exchange rate, and inflation	1990–2019	ARDL	Renewable energy causes currency appreciation
Deka et al. 64	E7 economies	Renewable energy use and exchange rate	1990–2019	ARDL	Renewable energy promotion boosts the appreciation of the exchange rate
Deka and Cavusoglu 65	OECD counties	Renewable energy use and exchange rate	2000–19	GMM and Bayesian VAR model	Renewable energy causes currency appreciation
Deka and Dube 18	Mexico	Renewable energy and exchange rate	1990–2019	ARDL	Renewable energy development decreases exchange rates
Green energy policies, carbon neutrality, and energy poverty
Deka et al. 19	Brazil	Renewable energy use and inflation	1990–2019	ARDL	Rate of inflation influences renewable energy use
Gakuru et al. 76	38 SSA countries	Renewable promotion and energy poverty	2000–22	MMQR	Renewable energy promotion alleviates energy poverty
Baz et al. 34	Pakistan	Energy consumption and environmental quality	1971–2014	NARDL	Renewable energies promote carbon neutrality
Usman and Balsalobre-Lorente 66	10 new industrialized countries	Renewable energy and carbon emissions	1990–2019	AMG	Renewable energy reduces carbon emissions
Dell’Anna 67	Italy	Energy efficiency and carbon neutrality	2010 onwards	Input–output method	Energy efficiency promotes carbon neutrality
Abbas et al. 30	5 BRICS	Renewable energy development and CO2	1990–2020	Panel NARDL, MG, and PMG	Renewable energy development alleviates CO2
Wen et al. 68	5 South Asian countries	Green innovation and environmental quality	1990–2014	FMOLS and DOLS	Green innovation promotes environmental quality
Zha et al. 69	China	Renewable energy and CO2	2006–19	DID method	Renewable energy policy alleviates CO2
Meng and Yu 70	China	Renewable energy, carbon tax, and CO2	2020 onwards	SD and equilibrium model	Renewable energy promotes CO2 alleviation
Lee et al. 71	China	Renewable energy and carbon neutrality	2024	Game theory	Renewable energy enhances carbon neutrality
Gang et al. 72	MENA region	Energy efficiency, green investment, and CO2	2004–19	CS-ARDL	Two elements contribute to CO2 reduction
Akpanke et al. 73	15 West African countries, 7 developed countries, and 7 emerging countries	Energy efficiency, renewable energy, and CO2	1990–2019	CS-ARDL, CCEMG, and AMG	Energy efficiency and renewable energy reduce CO2
Chen et al.47,48	China	Energy efficiency and carbon neutrality	1995–2018	Three-stage slacks-based model	Energy efficiency promotes carbon neutrality
Lee et al. 74	China	Green finance and energy transition	2011–20	MMQR	Green energy policies reduce energy poverty
Lee et al. 75	China	Green finance and energy inequality	2006–18	Fixed-effects model	Green finance alleviates energy inequality
Gakuru et al. 76	36 SSA countries	Renewable energy promotion and energy poverty	2000–22	MMQR	Renewable energy promotion alleviates energy poverty
Simionescu et al. 77	European Union	Renewable energy policy, energy efficiency, and energy poverty	2003–21	DOLS and MMQR	Energy efficiency decreases energy poverty
FDI, carbon neutrality, and energy poverty
Simionescu et al. 77	European Union	FDI and energy poverty	2003–21	DOLS and MMQR	FDI decreases energy poverty
Ma et al. 78	Global level	Forms of FDI and carbon emissions	2005–16	Network approach	FDI increases carbon emissions
Rauf et al. 79	BRICS	FDI and environmental quality	1990–2018	AMG and CCEMG	FDI increases CO2
Mahalik et al. 80	India	FDI and environmental quality	1978–2014	ARDL	FDI increases CO2
Mukiyen Avcı 81	Turkey	FDI and environmental quality	1984–2018	DOLS	FDI increases CO2
Xin-gang et al. 83	China	FDI and energy efficiency	2005–14	Spatial econometrics	FDI decreases energy poverty through promoting energy efficiency
Mimouni and Temimi 84	100 countries	FDI and energy efficiency	1980–2015	Panel data regressions	FDI decreases energy poverty through promoting energy efficiency
Hussain et al. 85	51 BRICS	FDI and renewable energy	1996–2017	Random-effects model and fixed-effects model	FDI enhances modern energy
Guang et al. 86	China	FDI and energy intensity	2000–16	Shapley decomposition regression and PQR	FDI influences energy intensity
Mohsin et al. 82	Latin American region	FDI and energy poverty	1995–2015	Pooled OLS and fixed-effects model	FDI alleviates energy poverty

Source: Work by the authors.

Abbreviations: FDI, foreign direct investment; ASEAN, Association of Southeast Asian Nations; GMM, generalized method of moment; OLS, ordinary least squares; OECD, Organization for Economic Co-operation and Development; MENA, Middle East and North Africa; VAR, Vector Autoregression; ARDL, Autoregressive Distributed Lag model; CUP-FM, Cross-sectional Unit Root and Panel Cointegration with Fixed Effects Model; CUP-BC, Cross-sectional Unit Root and Panel Cointegration with Bootstrap Correction; NARDL, Nonlinear Autoregressive Distributed Lag model; MG, Mean Group estimator; PMG, Pooled Mean Group estimator; FMOLS, Fully Modified Ordinary Least Squares; PSM, Propensity Score Matching; DID, Difference-in-Differences; QQ, Quantile-Quantile plot; CS-ARDL, Cross-Sectionally Augmented Autoregressive Distributed Lag model; SYS GMM, System Generalized Method of Moments; PQR, Panel Quantile Regression; FGLS, Feasible Generalized Least Squares; PCSE, Panel-Corrected Standard Errors; CGE, Computable General Equilibrium; EAC, East African Community; MMQR, Method of Moments Quantile Regression; AMG, Augmented Mean Group estimator; DOLS, Dynamic Ordinary Least Squares; SD, System Dynamics model; CCEMG, Common Correlated Effects Mean Group estimator; SSA, Sub-Saharan Africa.

Theoretical analysis

Referring to the idea of Wang et al., ⁸⁸ a theoretical analysis was conducted to provide a foundation for designing the conceptual framework (Figure 2). First, environmental Kuznets curve (EKC) is a theoretical model that suggests an inverted U-shaped relationship between environmental degradation and economic development. Initially, as an economy grows, environmental degradation tends to increase due to industrialization and urbanization. However, after reaching a certain level of income per capita, the trend reverses, and further economic growth leads to improved environmental quality. ⁸⁹ This may occur because developed societies being examined in this study often have more resources to invest in cleaner technologies, stricter environmental regulations, and greater public awareness of environmental issues. Referring to this study, the EKC implies that MP can ultimately contribute to environmental sustainability, and it also highlights the need for effective policies to manage the transition.

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Figure 2.

Conceptual framework indicating potential pathway for FDI and green energy policies, their consequences on the carbon neutrality and clean energy poverty. The novel macroeconomic performance index = (ΔGDP growth + ΔEmployment rate + ΔTrade intensity per capita) − (ΔInflation rate + ΔLending rate + ΔExchange rate). FDI, foreign direct investment.

Second, the sustainable development theory posits that development should meet the needs of the present without compromising the ability of future generations to meet their own needs. This theory emphasizes a balanced approach to economic growth, social inclusion, and environmental protection, recognizing that these three pillars are interdependent. ⁹⁰ It advocates for policies and practices that promote resource efficiency, reduce environmental degradation, and enhance the quality of life for all, while ensuring that economic activities do not deplete natural resources or harm ecological systems. This aligns with this study as the policy recommendations intend to provide new insights regarding promoting the transition towards a low-carbon economy through promoting clean energy, FDI, and MP.

Third, the Porter hypothesis posits that environmental regulations can enhance a firm's competitiveness by encouraging innovation and efficiency. Proposed by economist Michael Porter, it suggests that well-designed environmental policies can lead to reduced production costs and improved resource efficiency, ultimately driving technological advancements. ⁹¹ This hypothesis challenges the traditional view that regulations impose burdens on businesses, arguing instead that they can stimulate economic growth and create a competitive advantage, especially for firms that proactively adapt to regulatory challenges. This theory supports this study through examining the impacts of GEP on MP in higher-income countries.

Fourth, the structural theory of poverty posits that poverty is not merely the result of individual failings or poor choices, but rather a consequence of systemic factors and institutional structures that perpetuate economic inequality. This theory emphasizes the role of social, economic, and political frameworks that create barriers to opportunities, such as inadequate education, discrimination, and lack of access to resources. ⁹² This study incorporates the structural theory of poverty in the field of energy economics because poverty in energy access is often a result of institutional structures rather than individual choices.

Fifth, the pollution halo hypothesis suggests that strict environmental regulations in a country can actually help businesses become more competitive. This happens because these regulations encourage companies to innovate and develop cleaner technologies. ⁹³ As a result, countries with strong environmental policies can attract investment and industries that prioritize sustainability, leading to economic benefits and a cleaner environment. In simple terms, it means that good environmental laws can make businesses better and more appealing. This study verifies this hypothesis by examining whether FDI decreases CO₂ emissions.

Sixth, the pollution haven hypothesis means that companies might move their factories or operations to countries where environmental laws are weak or not enforced. This is done to save money on pollution control costs and maximize profits. Essentially, it suggests that countries with less strict environmental regulations can attract industries that produce a lot of pollution, leading to more environmental harm in those areas. ⁹³ This study verifies this hypothesis by examining whether FDI increases CO₂ emissions. Therefore, considering the above theoretical analysis, the E3 framework ²⁸ initiated by Cambridge econometrics was adopted. It emphasizes the need to balance economic development with environmental protection by analysing how energy policies impact economic performance while considering environmental consequences as recently claimed by Wang et al. ⁹⁴ It incorporates many theories such as EKC, sustainable development theory, energy transition theory, Porter hypothesis, structural theory of CEP, and pollution halo/haven theories. This E3 model was employed in constructing conceptual framework and empirical models. Analysing the effects of green energy climate policies on FDI patterns while accounting for CEP and environmental consequences has found E3 model helpful as indicated by the following model.

As a matter of fact, for improving results of the previous study, another study is needed to provide a deeper understanding by analysing the dynamic effects of shocks and changes in each variable in order to clearly identify the causal relationship and factors that explain more variability in carbon neutrality and CEP. Lastly, the previous studies have not been able to give an answer to the twin problems of energy (carbon emissions and CEP). As a crucial aspect, the literature on development needs to compare the consequences of both green climate policies and FDI on clean energy-related poverty and carbon neutrality. This is the present study's necessity as the above hypotheses are linked to the mentioned literature gaps. The conceptual model (Figure 2), summarizes and puts together all mechanisms from green energy climate policies to FDI and carbon neutrality implications using an analytical framework of the study.

Summarization of the data

This paper relied on World Bank database data that has changed throughout time, spanning from 1995 to 2022. ²⁴ The chosen nations comprise 77 high-income countries (Appendix 1). The mediating variable in this study is the novel MPI quantified by this study. This metric was quantified by improving the already existing MP metrics such as the misery index measure initiated by Gakuru and Yang, ⁹⁵ MPI with five indicators and economic (ECO)–MPI with seven indicators initiated by Mohanty et al. ²⁷ Our index also improved the ECO–MPI made of six indicators pointed out by Mohanty and Sahoo. ⁹⁶ Further information on the research variables is given in Table 2 and also, in Table 3, our novel index was validated or benchmarked against the established metrics. Per capita trade intensity variable in our new index implies trade-to-GDP ratio, measuring the degree to which a country engages in international trade. It was quantified as ( ( X P + M P ) / G D P P ) × 100 , where XP stands for the export per capita, MP refers to the import per capita, and GDPP is the GDP per capita. The basis for data analysis, guiding the choice of relevant methods, theories, and framing of research questions, was determined by descriptive statistics given in Table 4.

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Table 2.

Explanations of variables.

Variables	Explanations	Short	Source
Novel macroeconomic performance indexM	(ΔGDP growth + ΔEmployment rate + ΔTrade intensity per capita) − (ΔInflation rate + ΔLending rate + ΔExchange rate)	MP	WDI
Macroeconomic performance index by Ekren et al. 39 M	(ΔGDP growth rate+ΔEmployment rate) − (ΔInflation rate + ΔLending rate + ΔExchange rate)	MPI	WDI
Foreign direct investmentD	FDI, net inflows % of GDP	FDI	WDI
Carbon neutralityDT	CO2 emissions % change, https://data.worldbank.org/indicator/EN.ATM.CO2E.GF.KT	CO2	WDI
Renewable energy promotionIND	Renewable energy share (%): (Renewable Energy consumption/Total Energy Consumption) × 100. This is a crucial metric for assessing the success of renewable energy promotion efforts). Renewable energy share of electricity generation.	REP RP	WDI IEA
Energy efficiency improvementIND	The amount of energy needed for generating a single unit of economic production, which is commonly measured in GDP. More efficient use is implied by the decreased intensity	EI	WDI
Clean energy povertyDT	Clean energy development proxied by accessibility to clean fuels and cooking technologies, % of population	CED	WDI
CapitalC	Gross fixed capital formation in % of GDP	K	WDI
Industrial developmentC	Industry, value added in % annual growth	IND	WDI
Financial developmentC	Credit extended by banks to the private sector % of GDP	FD	WDI
Population growthAC	Annual % growth of population	PG	WDI
UrbanizationAC	The % of the total inhabitants that lives in cities	UB	WDI

Source: Work by the authors.

Abbreviations: M stands for a mediating variable, D refers to a dependent variable, DT stands for dependent variables in transmissions mechanism models, IND means independent variables, C refers to control variables and AC stands for additional control variables.

Note: The dataset includes yearly data from 1995 through 2022.

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Table 3.

Novel macroeconomic performance-related indices.

Indices	Description	Source	Why the choice of novel macroeconomic performance index (MPI)
ECO–MP	The ECO–MPI consists of six key macroeconomic indicators: economic growth, employment, trade, inflation, fiscal deficit, and pollution.	Mohanty and Sahoo 96	MP is an excellent measure as it takes into account the dynamics of exchange rates and lending rates within a country's economy.
MPI	(ΔGDP growth rate + ΔEmployment rate) − (ΔInflation rate + ΔLending rate + ΔExchange rate)	Ekren et al. 39	MP is the best measure, as it accounts for trade intensity. This explains the level of engagement in international trade.
MP	The MPI is formulated based on five indicators: economic growth, employment, trade, inflation, and fiscal deficit.	Mohanty et al. 27	MP is the best measure, as it considers the dynamics of exchange rates, lending rates, and trade intensity.
ECO–MP	The ECO–MPI comprises seven indicators, incorporating environmental factors such as pollution and climate change, which significantly affect the economic performance. Specifically, pollution is measured by CO2 levels, while climate change is assessed through mean temperature. Other five factors are economic growth, employment, trade, inflation, and fiscal deficit.	Mohanty et al. 27	MP is the most effective measure because it incorporates the dynamics of exchange rates, lending rates, and trade intensity.
Improved misery index (MI)	(ΔUnemployment rate + ΔInflation rate + ΔLending rate + ΔExchange rate) − (ΔGDP growth rate)	Gakuru and Yang 95	MP is the best measure as it takes into account the dynamics of trade intensity.
Novel macroeconomic performance index	(ΔGDP growth + ΔEmployment rate + ΔTrade intensity per capita) − (ΔInflation rate + ΔLending rate + ΔExchange rate)	This work	This allows for the quantification of the country's macroeconomic performance and includes several indicators that were overlooked in previous literature.

Source: Authors gathered information from various literature sources related to indices on macroeconomic performance.

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Table 4.

Descriptive statistics.

Variables	Mean	Median	Max	Min	SD	Obs.
FDI	12.746	12.518	15.806	6.549	1.272	2156
MP	12.157	12.423	15.584	−3.914	1.901	2156
MPI	10.934	9.067	14.611	−1.956	1.841	2156
CED	98.276	99.944	99.999	86.456	3.989	2156
CO2	3.058	2.573	9.396	−7.652	3.781	2156
EI	4.743	4.715	5.667	3.461	0.552	2156
FD	142.751	139.829	162.017	132.721	8.196	2156
PG	0.491	0.521	0.728	−0.071	0.151	2156
REP	19.091	18.665	24.987	6.474	2.426	2156
RP	28.525	26.282	38.854	14.097	4.632	2156
UB	78.597	78.594	85.647	16.127	0.191	2156
K	22.381	22.309	23.904	20.643	0.962	2156
IND	1.662	2.152	6.738	−9.303	2.952	2156

Source: Work by the authors.

Econometrics model

The study's main focus is on the processes that green energy climate policies rely on to increase FDI. The first model examines the baseline model, the second and third models are about mechanism analysis, and the fourth and fifth models involve heterogeneous transmission mechanisms to CO₂ and clean energy development (CED), respectively.

Model 1:

F D I i t = f ( R E P i t , E I i t , K i t I N D i t , F D i t )

(1)

Model 2:

M P = f ( R E P i t , E I i t , K i t , I N D i t , F D i t )

(2)

Model 3:

F D I i t = f ( R E P i t , E I i t , M P i t , K i t I N D i t , F D i t )

(3)

Model 4:

C O 2 i t = f ( R E P i t , E I i t , M P i t , F D I i t )

(4)

Model 5:

C E D i t = f ( R E P i t , E I i t , M P i t , F D I i t )

(5)

Model 1:

ln F D I i t = a 0 i + a 1 i ln R E P i t + a 2 i ln E I i t + a 3 i ln K i t + a 4 i ln I N D i t + a 5 i ln F D i t + e i t

(6)

Model 2:

ln M P i t = a 0 i + a 1 i ln R E P i t + a 2 i ln E I i t + a 3 i ln K i t a 4 i ln I N D i t + a 5 i ln F D i t + e i t

(7)

Model 3:

ln F D I i t = a 0 i + a 1 i ln R E P i t + a 2 i ln E I i t + a 3 i ln M P i t + a 4 i ln K i t + a 5 i ln I N D i t + a 6 i ln F D i t + e i t

(8)

Model 4:

ln C O 2 i t = a 0 i + a 1 i ln R E P i t + a 2 i ln E I i t + a 3 i ln M P i t + a 4 i ln F D I i t + e i t

(9)

Model 5:

ln C E D i t = a 0 i + a 1 i ln R E P i t + a 2 i ln E I i t + a 3 i ln M P i t + a 4 i ln F D I i t + e i t

(10)

Reasons for selecting the econometric techniques used

The study utilized the method developed by Driscoll and Kraay, ¹⁰¹ which is recognized for its flexibility in addressing issues such as heterogeneity, heteroscedasticity, serial correlation, and cross-sectional dependence. This makes it a popular choice for panel data analysis. This approach was particularly suitable for this research due to the large number of cross-sectional units compared to the limited time periods involved. This model effectively takes into account differences across various countries and time periods, as highlighted by Acheampong et al. ¹⁰² Consequently, the results from the fixed-effects model developed by Driscoll and Kraay ¹⁰¹ are favoured due to their superior reliability and comprehensiveness compared to pooled ordinary least squares (OLS) and random effects models.

Pooled OLS approach analyses panel data as a whole to derive average effects, based on the assumption of uniform relationships across various entities and time periods, which may overlook important heterogeneity. ¹⁰³ In contrast, a random effects model assumes that individual-specific effects are uncorrelated with the explanatory variables, a premise that may often be violated in practical situations. Additionally, the method proposed by Driscoll and Kraay ¹⁰¹ was favoured over generalized method of moments due to the lack of appropriate instruments within the dataset. Figure 3 presents a range of econometric methods employed for various purposes. The panel quantile regression method was chosen to address both distributional concerns and the issue of unobserved individual heterogeneity, building on the foundational research by Koenker and Bassett. ¹⁰⁴

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Figure 3.

Empirical flowchart. Source: Authors’ illustration.

To tackle the issue of endogeneity, we employed a two-stage least squares (2SLS) method as outlined by Kelejian, ¹⁰⁵ utilizing an instrumental variable (IV) approach. However, finding appropriate external IVs for foreign financial transactions was challenging. Consequently, we adopted the strategies also used by Wen et al. ¹⁰⁶ and Mondjeli et al., ¹⁰⁷ using lagged values of the independent variables as instruments. To confirm and compare the transmission effects, we applied the panel SVAR model, which accounts for cross-sectional dependence and can address long-term dynamic relationships, following the methodology described by Pedroni. ¹⁰⁸ The impulse response (IR) and variance decomposition (VD) methods detailed by Lanne and Nyberg ¹⁰⁹ were utilized to quantify the transmission effects.

The construction of the novel MPI

The novel MPI quantified with (ΔGDP growth + ΔEmployment rate + ΔTrade intensity per capita) − (ΔInflation rate + ΔLending rate + ΔExchange rate) ignored weights (Table 5). This is because, assigning different weights could introduce bias based on subjective judgments about the importance of each indicator. Equal weights eliminate this subjectivity, promoting fairness and objectivity in the evaluation process. The choice of equal weights aligns with the ideas of Okun Arthur ¹¹⁰ and Barro ¹¹¹ who first initiated the indexes for MP even though, they named them misery indices. ⁹⁵ The components of the study's index are a mix of positive indicators (e.g. ΔGDP growth, ΔEmployment rate, ΔTrade intensity per capita) and negative indicators (e.g. ΔInflation rate, ΔLending rate, ΔExchange rate). This makes it impossible to use principal component analysis when quantifying the novel MPI. Again, since the MPI is composed of both positive and negative indicators, and macroeconomic indicators are often interdependent, the entropy method adopted by Wang et al. ¹¹² is not appropriate in this context, as it assumes that the variables are independent and only used positive indicators.

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Table 5.

Construction of the novel macroeconomic performance index.

Indicators	Calculation	Details	Source
Annual GDP growth rate	G D P t − G D P t − 1 G D P t − 1 × 100	GDP means gross domestic product, t stands for current year, t − 1 refers to previous year.	World Bank 24
Annual employment rate	E M W A × 100	EM stands for number of employed individuals; WA refers to working-age population.	World Bank 24
Annual trade intensity rate	X P − M P G D P P × 100	XP refers to export per capita, MP implies import per capita.	World Bank 24
Annual inflation rate	G D P D t − G D P D t − 1 G D P D t − 1 × 100 G D P D = N G D P R G D P × 100	GDPD refers to GDP deflator and t stands for current year, t − 1 refers to previous year. NGDP refers to nominal GDP and RDGP refers to Real GDP.	World Bank 24
Annual lending rate	I P L A × 100	IP means interest paid and LA refers to loan amount.	World Bank 24
Annual exchange rate	E X t − E X t − 1 E X t − 1 × 100	EX means exchange rate, t stands for current year, t − 1 refers to previous year.	World Bank 24

Source: Author's development.

Note: All units are % and all indicators have equal weights.

Preliminary tests

Cross-sectional dependence tests

The findings in Table 6 demonstrated cross-sectional dependability, which was validated employing the cross-sectional dependence test introduced by Pesaran. ¹¹³ All of the probability values that are less than 0.05 confirm these findings. This indicates that a disturbance in one nation can influence others. This observation underscores the importance of employing second-generation unit root testing techniques that consider cross-sectional dependence, as opposed to first-generation methods that operate under the assumption of cross-sectional independence.

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Table 6.

Cross-sectional dependence tests.

Variables	BPLM	PSLM	BSLM	PCD
REP	5233.266***	176.584***	175.917***	36.656***
EI	6843.557***	235.150***	234.483***	82.419***
MP	545.589***	60.951***	54.284***	28.852***
MPI	4240.042***	140.461***	139.794***	55.541***
FDI	2136.708***	63.963***	63.296***	38.766***
FD	1665.446***	46.823***	46.157***	15.367***
PG	608.997***	85.401***	74.734***	42.234***
RP	713.590***	22.205***	18.538***	11.573***
UB	2795.580***	87.926***	87.259***	22.334***
K	1438.273***	38.561***	37.895***	12.841***
IND	7283.722***	251.158***	250.492***	61.570***
CED	1229.095***	30.954***	30.287***	17.902***
CO2	2231.708***	65.461***	62.146***	39.7268***

Source: Authors’ analysis.

Abbreviations: BPLM, Breusch–Pagan Lagrange multiplier; PSLM, Pesaran-scaled Lagrange multiplier; BSLM, bias-corrected-scaled Lagrange multiplier; PCD, Pesaran cross-sectional dependence.

***p < 0.01.

Stationarity test

To prevent spurious regression results and improve the reliability and precision of the statistical analysis, a stationarity test was performed (see Table 7). This included the use of the CIPS (cross-sectional ¹¹⁴ ) and CADF (cross-sectional augmented Dickey–Fuller) tests. The results indicated that all variables were stationary following first-order differencing.

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Table 7.

Stationarity test.

	Level form		First difference
Variables	CIPS	CADF	CIPS	CADF
Constant
REP	−3.489	−4.895	−3.882***	−5.865***
EI	−3.348	−4.652	−4.876**	−5.884**
MP	−2.479***	−4.929***
MPI	−3.376	−4.674	−3.938**	−5.112**
FDI	−4.185***	−5.836***
FD	−3.296	−4.586	−4.783***	−5.865***
PG	−4.142***	−5.835***
RP	−3.732***	−5.885***
UB	−3.685***	−5.827***
K	−4.945***	−5.829***
IND	−3.767**	−4.873**
CED	−2.689	−4.687	−5.165***	−5.862***
CO2	−3.938*	−4.872*
Constant and trend
REP	−3.166	−4.526	−3.628***	−5.446***
EI	−1.987	−4.113	−3.995**	−5.918**
MP	−3.536***	−5.413***
MPI	−2.816	−4.127	−3.121**	−4.538**
FDI	−1.289***	−1.974***
FD	−2.982	−3.887	−4.584***	−5.418***
PG	−3.812***	−5.416***
RP	−3.732***	−5.414***
UB	−3.748***	−5.418***
K	−3.594***	−5.469***
IND	−3.259**	−4.526**
CED	−3.154	−3.878	−4.885***	−5.449***
CO2	−4.173*	−5.787*

Source: Authors’ analysis.

Abbreviation: CADF, cross-sectional augmented Dickey–Fuller; CIPS, cross-sectionally augmented Im, Pesaran, and Shin test.

* stands for p < 10 % , * * stands for p < 5 % , and * * * stands for p < 1 % .

Panel cointegration test

The study conducted the Kao cointegration tests to determine if long-term relationships exist among the variables. The findings, shown in Table 8, confirm that the Kao tests indicate the presence of long-term relationships among the variables. Thus, the variables chosen for this study are suitable for further regression analysis. This is due to all probability values being less than 5%.

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Table 8.

Kao cointegration tests results.

Models	t-Statistics	Prob.
Model 1: FDI as dependent variable
ADF	−4.981	0.000
Residual variance	0.674
HAC variance	0.556
Model 2: MP as dependent variable
ADF	−3.377	0.000
Residual variance	0.059
HAC variance	0.024
Model 3: FDI as dependent variable
ADF	−8.998	0.000
Residual variance	91.700
HAC variance	46.317
Model 4: CO2 as dependent variable
ADF	1.842	0.032
Residual variance	10.825
HAC variance	15.193
Model 5: CED as dependent variable
ADF	−1.961	0.024
Residual variance	21.686
HAC variance	20.220

Note: Refer to Kao ¹¹⁵ for the formulas utilized.

Abbreviations: FDI, foreign direct investment; ADF, augmented Dickey–Fuller; HAC, Heteroskedasticity and autocorrelation consistent.

Multicollinearity test

Estimation efficiency challenges often root from multicollinearity, especially when the correlation coefficients between the independent variables exceed 0.8. ¹¹⁶ The correlation among all variables is depicted in the heat map (see Figure 4). The results indicated the absence of multicollinearity problem as well as the interactions among variables. This proves data quality, potential coefficients stability in the model, and potential reliable estimates.

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Figure 4.

Correlation matrix heat map indicating the association among the variables. Pearson's values (r), ***p < 0.01, **p < 0.05, *p < 0.1. Source: Authors’ analysis.

Estimates’ results and discussion

Baseline models’ findings and first hypothesis verification

In the results from model 1 based on Driscoll–Kraay estimates, which investigates the effect of GEP on FDI, it was found that these policies (REP and EI) have a positive effect on FDI. By comparing the effects of REP and EI (Table 9), it was indicated that a 1% increase in REP leads to a 0.57% rise in FDI, while a 1% increase in EI results in a 0.24% rise in FDI. This indicates that REP has a stronger positive effect on FDI compared to EI. Therefore, hypothesis 1, which states that GEP enhance FDI, is valid. This is supported by the significance of the REP coefficient at 1% and the significant coefficient of EI at 5% in the Driscoll–Kraay estimates in the first column of Table 9. This suggests that promoting sustainable energy practices can attract more international investment, signalling confidence in the nation's dedication to the sustainability of the environment and economic growth. This evidence is reinforced by Leitão et al., ³¹ who indicated that renewable energy consumption increases FDI in the Visegrad countries, and by Pan et al., ²¹ who found that carbon finance policies attract FDI in China. Unfortunately, the above stated literatures do not provide insights into how MP influences this relationship. The next subsection explores the analysis of a novel MPI mechanism within the identified GEP–FDI nexus.

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Table 9.

Results derived from the fixed-effects estimation using the Driscoll–Kraay method in the initial regression model and mechanism analysis.

Variables	Baseline model	Path analysis	Path analysis
	Model 1: FDI (DV)	Model 2: MP (DV)	Model 3: FDI (DV)
REP	0.572*** (6.269)	0.578*** (5.139)	0.374*** (6.631)
EI	0.249** (1.988)	0.364* (1.745)	0.152* (1.685)
MP			0.304*** (5.852)
K	0.641*** (4.773)	0.674*** (5.117)	0.469*** (6.624)
IND	0.751*** (6.271)	0.894*** (7.863)	0.823*** (7.854)
FD	0.195** (2.201)	0.397* (1.712)	0.305** (2.395)
Constant	0.647*** (10.452)	0.802*** (12.606)	0.764*** (9.423)
Observations	2156	2156	2156
R 2	0.682	0.728	0.703
Country FE	Yes	Yes	Yes
Time FE	Yes	Yes	Yes

Source: Authors’ analysis.

Abbreviations: FDI, foreign direct investment; DV, dependent variable; MP, macroeconomic performance.

Note: t-values in parentheses.

*Stands for p < 10 % , ** stands for p < 5 % , and *** stands for p < 1 % .

GEP transmission mechanisms to carbon neutrality, CEP, and third hypothesis verification

In the fourth model, all energy-based climate policies (REP and EI) have a negative effect on CO₂. According to the Driscoll–Kraay estimates presented in Table 10, the first column indicates that a 1% increase in REP leads to a 0.27% reduction in CO₂, while a 1% increase in EI results in a 0.13% decrease in CO₂. This indicates that REP has a more pronounced negative effect on CO₂ compared to EI. These energy-based climate policies in developed countries drive down CO₂ emissions by reducing fossil fuel dependence, increasing energy efficiency, and promoting the adoption of renewable energy and clean technologies, ¹¹⁷ supported this negative nexus in top wind energy generation countries. The novel MP and FDI exhibits positive effects on CO₂ in higher-income countries. This implies that economic growth and investment activities are associated with environmental degradation as also mentioned by Gang et al. ⁷² This relationship suggests that as economies expand and attract FDI, the increased industrial activity, energy consumption, and infrastructure development often rely on carbon-intensive processes, leading to higher CO₂ emissions. Such a finding underscores the environmental trade-offs of economic growth and globalization, highlighting the need for sustainable practices, cleaner technologies, and policies that decouple economic and investment growth from environmental harm to mitigate climate change risks. ¹⁴

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Table 10.

Results derived from the fixed-effects estimation using the Driscoll–Kraay method for transmissions to carbon neutrality and clean energy poverty analysis.

Variables	Model 4: CO2 (DV)	Model 5: CED (DV)
REP	−0.278*** (5.162)	0.392*** (6.852)
EI	−0.137* (1.753)	0.272** (2.266)
MP	0.376** (2.179)	0.587*** (4.974)
FDI	0.479*** (5.663)	0.477*** (8.643)
Constant	0.873*** (12.159)	0.714*** (10.622)
Observations	2156	2156
R 2	0.695	0.714
Country FE	Yes	yes

Source: Authors’ analysis.

Abbreviations: DV, dependent variable; CED, clean energy development.

Note: t-values in parentheses.

*Stands for p < 10 % , ** stands for p < 5 % , and *** stands for p < 1 % .

In the fifth model (Table 10), GEP (REP and EI) have positive effects on CED. This means that GEP alleviate CEP, since CED is a reverse indicator of CEP. According to the Driscoll–Kraay estimates shown in Table 10, the second column indicates that a 1% increase in REP results in a 0.39% increase in CED (a reduction in CEP), while a 1% rise in EI leads to a 0.27% increase in CED. This suggests that REP has a more significant positive impact on CED compared to EI. The GEP in higher-income countries drive CED by creating a supportive regulatory environment, attracting investment, fostering technological innovation, enhancing energy security, and providing environmental and economic benefits. In this context, Kocak et al. ¹¹⁹ confirm that effective energy policies lead to strong outcomes in energy security and environmental sustainability in developed countries; however, their examination was limited to electricity generation. These findings of negative effects on CEP are similar to some studies in the literature. For instance, Simionescu et al., ⁷⁷ who revealed the significant impact of renewable energy policies on reducing energy poverty in EU member countries. However, all the studies in literature talked about energy poverty in general. Therefore, hypothesis 3, which states that GEP enhance carbon neutrality and alleviate CEP, is valid. This is supported by the significance of the negative coefficient of REP at the 1% level and the significant negative coefficient of EI at the 10% level in the Driscoll–Kraay estimates in the first column of Table 10, where carbon emissions are the dependent variable. Additionally, it is further supported by the significance of the positive coefficient of REP at the 1% level and the significant negative coefficient of EI at the 5% level in the second column of Table 10, where CED is the dependent variable.

Robustness analysis

The replacement of the main independent variables and additional control variables

This study uses the percentage of renewable energy in total electricity (RP) as a proxy for REP and also incorporates the MPI developed by Ekren et al. ³⁹ Additionally, the study includes other control variables, such as population growth and urbanization, to conduct robustness checks (Tables 11 and 12). Many scholars, including Peng et al. ¹²⁰ and Lee and Zou, ¹²¹ have recognized this approach to robustness checks. The consistent signs of the coefficients and their statistical significance confirm that the study's results remain robust, even after accounting for the replacement of the main independent variables and additional control variables.

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Table 11.

Robustness checks with additional control variables and the replacement of the main independent variable in the baseline and mechanism models using Driscoll–Kraay method.

Variables	Model 1: FDI (DV)	Model 2: MP (DV)	Model 3: FDI (DV)
REP			0.352*** (4.07)
RP	0.482*** (7.356)	0.521*** (6.159)
EI	0.275*** (3.695)	0.359** (2.148)	0.113** (2.223)
MPI			0.286*** (8.832)
K	0.573*** (6.937)	0.521*** (5.762)	0.458*** (7.585)
IND	0.586*** (7.673)	0.611*** (7.281)	0.712*** (6.989)
FD	0.183* (1.695)	0.441** (2.334)	0.2582** (2.191)
PG	0.259*** (4.741)	0.479** (2.285)	0.327** (2.036)
UB	0.368** (1.998)	0.469*** (6.876)	0.376*** (5.865)
Constant	0.682*** (8.146)	0.732*** (10.248)	0.702*** (8.683)
Observations	2156	2156	2156
R 2	0.668	0.702	0.694
Country FE	Yes	Yes	yes
Time FE	Yes	Yes	Yes

Source: Authors’ analysis.

Abbreviations: FDI, foreign direct investment; DV, dependent variable; MP, macroeconomic performance.

Notes: t-values in parentheses.

*Stands for p < 10 % , ** stands for p < 5 % , and *** stands for p < 1 % .

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Table 12.

Robustness checks with additional control variables and the replacement of the main independent variable in the transmissions’ models analysis using Driscoll–Kraay method.

Variables	Model 4: CO2 (DV)	Model 5: CED (DV)
RP	−0.224*** (4.329)	0.371*** (7.439)
EI	−0.101** (2.154)	0.241*** (4.874)
MP	0.426* (1.658)	0.505*** (4.162)
FDI	0.598*** (6.004)	0.492*** (5.428)
PG	0.328*** (5.543)	0.167** (2.139)
UB	0.438** (6.758)	0.493*** (6.973)
Constant	0.764*** (9.264)	0.673*** (12.742)
Observations	2156	2156
R 2	0.709	0.737
Country FE	Yes	Yes

Source: Authors’ analysis.

Abbreviations: DV, dependent variable; CED, clean energy development.

Notes: t-values in parentheses.

*Stands for p < 10 % , ** stands for p < 5 % , and *** stands for p < 1 % .

Endogeneity analysis

Endogeneity identification was conducted through the following steps. First, the original regression equation of the model is constructed. Then, the first explanatory variable is made the dependent variable, and an OLS regression is conducted. The residuals from this initial regression are stored. Next, the original equation is modified by replacing the independent variable with the residuals obtained in the previous step. If the p-value of the residual variable in this modified regression is statistically significant (p < 0.05), it suggests that endogeneity is present in the original independent variable. If p > 0.05, it indicates the absence of endogeneity. Finally, the entire process is repeated, considering other explanatory variables in the model. This approach aims to detect the presence of endogeneity, which occurs when the independent variable is correlated with the error term, leading to biased and inconsistent estimates as adopted also by Gakuru and Yang. ⁹⁵

Through the above steps, the endogeneity problem was tested and addressed in all the five models. In case the presence of endogeneity, the variable is called endogenous, and in its absence, the variable is called exogenous (Table 13). The test indicated that only industrial development in the first model, financial development in the second model, industrial development in the third model, and EI in the fifth model are exogenous among all the variables of the study. The presence of endogeneity in the other variables justified the use of an IV approach, specifically 2SLS, to ensure consistency and robustness in addressing the endogeneity issue (see Table 14). At this point only endogenous variables experienced lags in the 2SLS model. The 2SLS estimates have been provided for the first to the fifth lags of the independent variables. The third lag was chosen as the best lag for the model based on the results of the IV diagnostic tests. These IV diagnostic tests include weak instrument tests and overidentification tests, as reported in Table 14.

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Table 13.

Endogeneity identification.

Residuals	Coefficients	t-Statistic	Probability	Decision
Model 1: FDI as dependent variable
Resid_REP	0.579	19.407	0.000	Endogenous
Resid_EI	0.399	5.967	0.000	Endogenous
Resid_K	0.706	9.967	0.000	Endogenous
Resid_IND	0.059	1.261	0.208	Exogenous
Resid_FD	0.443	6.893	0.000	Endogenous
Model 2: MP as dependent variable
Resid_REP	0.428	2.459	0.014	Endogenous
Resid_EI	0.397	3.547	0.000	Endogenous
Resid_K	0.485	4.645	0.000	Endogenous
Resid_IND	0.444	4.162	0.000	Endogenous
Resid_FD	0.095	1.705	0.088	Exogenous
Model 3: FDI as dependent variable
Resid_REP	0.314	4.259	0.000	Endogenous
Resid_EI	0.247	2.661	0.008	Endogenous
Resid_MP	0.571	14.531	0.000	Endogenous
Resid_K	0.367	5.294	0.000	Endogenous
Resid_IND	0.025	0.364	0.715	Exogenous
Resid_FD	0.439	2.817	0.005	Endogenous
Model 4: CO2 as dependent variable
Resid_REP	−0.363	−6.234	0.000	Endogenous
Resid_EI	−0.161	−4.491	0.000	Endogenous
Resid_MP	0.305	6.415	0.000	Endogenous
Resid_FDI	0.539	4.927	0.000	Endogenous
Model 5: CED as dependent variable
Resid_REP	0.482	2.521	0.011	Endogenous
Resid_EI	0.327	0.084	0.932	Exogenous
Resid_MP	0.542	3.896	0.000	Endogenous
Resid_FDI	0.337	3.488	0.000	Endogenous

Source: Authors’ analysis.

Abbreviations: FDI, foreign direct investment; MP, macroeconomic performance; CED, clean energy development.

Note: Resid refers to the residuals from a regression model.

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Table 14.

Endogeneity test in the main models.

Variables of interest	Baseline model	Path analysis	Paths analysis	Transmissions to CO2	Transmissions to CED
	IV(2SLS)	IV(2SLS)	IV(2SLS)	IV(2SLS)	IV(2SLS)
	Model 1: FDI (DV)	Model 2: MP(DV)	Model 3: FDI (DV)	Model 4: CO2 (DV)	Model 5: CED (DV)
REP	0.678***(9.745)	0.723***(11.643)	0.475***(8.479)	−0.367***(−3.698)	0.478***(5.594)
EI	0.379**(2.659)	0.475***(3.986)	0.347***(6.723)	−0.279**(−2.356)	0.368***(3.741)
MP			0.654***(10.543)	0.652***(8.379)	0.783***(9.652)
FDI				0.739***(9.201)	0.647***(8.305)
K	0.877***(12.734)	0.899***(13.768)	0.799***(14.864)
IND	0.956***(14.459)	0.986***(16.571)	0.864***(16.643)
FD	0.475**(7.783)	0.886***(15.674)	0.747***(13.753)
Constant	0.997***(16.748)	0.867***(14.578)	0.722***(12.768)	0.896***(15.785)	0.985***(16.503)
Observations	2156	2156	2156	2156	2156
R 2	0.735	0.757	0.702	0.683	0.701
Country FE	Yes	Yes	Yes	Yes	Yes
Time FE	Yes	Yes	Yes	Yes	Yes
Hansen J test	0.786	0.863	0.758	0.887	0.956
Cragg–Donald F	18.75	19.43	16.74	19.65	18.78
Pagan–Hall test	0.654	0.563	0.489	0.756	0.785
Breusch–Pagan test	0.852	0.679	0.895	0.971	0.659
AR(2)	0.794	0.837	0.745	0.769	0.749

Source: Authors’ analysis.

Abbreviations: CED, clean energy development; IV, instrumental variable; 2SLS, two-stage least squares; FDI, foreign direct investment; DV, dependent variable.

Note: t-values in parentheses.

*Stands for p < 10 % , ** stands for p < 5 % , and *** stands for p < 1 % .

The findings from the baseline model, mechanism analysis, transmissions analysis, and heterogeneity analysis represent significant contributions of this study. This is due to the results obtained while addressing the issue of endogeneity through the use of IV regression (Table 14). Controlling for endogeneity was very important due to the reverse causality indicated by the bidirectional relationship between REP and FDI.^12,22 Additionally, bidirectional causality was found between EI and FDI. ¹³ Furthermore, GDP and FDI also exhibit bidirectional causality.^15,38 Bidirectional causality was also observed between REP and CEP. ²⁹ The 2SLS approach was employed to address endogeneity concerns across the five models in this study. The validity of the IV(2SLS) estimates is supported by a high Cragg–Donald F statistic (F > 10), indicating that the instruments are robust. Additionally, the strength of the instruments is further confirmed by elevated Hansen J p-values (p > 0.05). Furthermore, both the Pagan–Hall test and the AR(2) test for the second-order autocorrelation yielded high p-values (p > 0.05), confirming the lack of heteroscedasticity and second-order autocorrelation, respectively.

To address endogeneity concerns, we reformulated the Driscoll–Kraay fixed-effects models by incorporating lags of the independent variables and applied 2SLS estimation techniques. The signs of the coefficients for the independent variables in the 2SLS estimation validate and reinforce the robustness of the panel Driscoll–Kraay fixed-effects results, despite the difficulties in identifying suitable external IVs for our explanatory variables. Therefore, we follow the approaches of Wen et al. ¹⁰⁶ and Lee and Zou ¹²¹ by utilizing the lagged values of the explanatory variables as instruments. The 2SLS results demonstrate that the findings of this paper are robust, remaining consistent also after changing econometric approaches, additional controls, and substitutions of explanatory variables. Overall, the results reaffirmed the strong effects of REP compared to the effects of EI across models (Figure 5). This offers a strong basis for policymakers, researchers, and economists to leverage insights from the 3E model in making informed policy decisions and promoting sustainable economic development in higher-income countries.

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Figure 5.

Summary comparing the effects of REP versus EI across models. Source: Authors’ illustrations. REP, renewable energy promotion; EI, energy efficiency improvements.

Heterogeneity analysis

About heterogeneity analysis in the baseline model, REP have a greater positive impact on FDI in countries with lower levels of FDI compared to those with higher levels (Figure 6). Conversely, EI has a stronger positive effect in countries with higher levels of FDI rather than in those with lower levels. The reason behind is that, in rich (developed) countries, promoting renewable energy often attracts more foreign investment (FDI) in places where there was not much investment before. This is because investors see a big opportunity in these countries the markets are still growing, and the promotion of clean energy shows that the government is serious and supportive. That makes investors feel more confident and willing to take the risk. But in countries that already have a lot of foreign investment, the market is often crowded, and there's less room to grow, so the same renewable energy policies do not make as big of a difference. In addition, regarding EI, it has a stronger positive effect on FDI inflows in countries that already have high levels of FDI because these countries often have better infrastructure, stable institutions, and more advanced technologies, making it easier and more profitable for investors to benefit from energy savings. In such environments, improving energy efficiency can significantly reduce operating costs for businesses, which attracts even more foreign investment. In contrast, countries with low FDI levels may lack the necessary infrastructure or supportive policies, so even if they improve energy efficiency, the overall business environment may still not be attractive enough to strongly boost FDI inflows. Again, the positive heterogeneous impact of GEP examined on FDI allowed us to reaffirm the first hypothesis that GEP enhance FDI.

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Figure 6.

Heterogeneity analysis. The blue dashed line represents the shift of coefficients for panel quantile regressions. The 95% confidence interval is shown by the dashed yellow lines. Source: Authors’ illustrations.

Concerning heterogeneity in mechanism analysis, GEP (REP and EI) have heterogeneous positive effects on MP (Figure 6). The stronger positive effects are observed in countries with higher MP compared to those with lower MP. These findings are obvious because countries have varying levels of green energy innovations ¹²¹ and economic policies have different capacities to enhance MP of countries.^118,122 About REP, in higher-income countries, the stronger positive effects of REP on MP are more evident in countries that already have higher MP because these countries typically have better infrastructure, stronger institutions, and more efficient markets to support and scale renewable energy initiatives. With stable economies, they can allocate more resources, attract greater private investment, and implement policies more effectively, leading to greater job creation, innovation, and energy cost savings. In contrast, countries with lower MP may face structural challenges such as limited funding, weaker governance, or slower policy implementation that reduce the impact of REP on their overall economic growth. Furthermore, in higher-income countries, EI have stronger positive effects on MP in nations with already high MP because these countries have the infrastructure, technology, and institutional capacity to fully leverage efficiency gains. High-performing economies can more effectively integrate advanced energy-saving technologies, reduce production costs, and boost productivity, which translates into greater economic growth. Additionally, these countries often have supportive policies, skilled labour, and access to capital, enabling faster and more impactful implementation of energy efficiency measures. In contrast, countries with lower MP may struggle with limited resources, outdated infrastructure, and weaker policy enforcement, which reduces the overall impact of EI on their economic performance.

Regarding heterogeneity analysis in the third model, the effects of the examined variables on FDI are heterogeneous (Figure 6). MP and IND have stronger positive effects in countries with higher FDI, but weaker positive effects in those with lower FDI. Again, REP have a greater positive impact on FDI in countries with lower levels of FDI compared to those with higher levels. Conversely, EI has a stronger positive effect in countries with higher levels of FDI rather than in those with lower levels. The findings imply that countries with FDI benefit more from MP, green energy development, and industrial development, indicating that policy efforts should focus on enhancing these areas to attract FDI. Conversely, countries with lower FDI may need to adopt targeted strategies to improve their macroeconomic conditions and industrial capabilities. These aspirations are in alignment with the current SDG 17, which underscores the necessity of global cooperation. ⁷ About MP as a variable of interest, in higher-income countries, stronger positive effects of MP on FDI inflows are observed in countries with higher existing FDI inflows because these countries tend to have more stable economic environments, better infrastructure, and well-established financial markets that create a favourable investment climate. High MP signals growth potential and reduces investment risks, which attracts even more foreign investors looking to capitalize on expanding opportunities. Additionally, countries with already substantial FDI benefit from network effects, such as better business ecosystems and skilled labour pools, making them more attractive destinations for additional investments. In contrast, countries with lower FDI inflows may face structural challenges or uncertainties that limit the impact of MP improvements on attracting new foreign investments.

About heterogeneity analysis in transmission models, in the fourth model, where CO₂ is the explained variable, the effects of the examined explanatory variables are heterogeneous (Figure 5). GEP (RE and EI) have greater negative impacts on CO₂ in countries with higher CO₂ levels compared to those with lower CO₂ levels. This is because, higher-income countries have more room for reduction and a greater reliance on fossil fuels, making policy interventions more impactful. When CO₂ emissions are already high, even modest shifts towards cleaner energy sources and more efficient energy use can lead to significant absolute reductions in emissions. Additionally, governments in high-emission countries may implement these policies more aggressively to meet international climate commitments, amplifying their effect. In contrast, countries with already low CO₂ levels have fewer emissions to cut, so the marginal impact of GEP tends to be smaller. In contrast, MP and FDI show stronger positive effects on CO₂ in countries with higher CO₂ levels rather than in those with lower levels. The reason is that, economic growth and increased foreign investment in these contexts are often linked to energy-intensive industries and greater consumption of fossil fuels. High CO₂-level countries typically have larger industrial bases and transportation networks that contribute more significantly to emissions as economic activity expands. As a result, improvements in GDP and inflows of FDI can lead to higher energy demand, which, in the absence of strict environmental regulations or a strong shift towards clean energy, results in greater CO₂ output. In contrast, countries with lower CO₂ levels may already have cleaner energy systems, stricter environmental policies, or less energy-intensive economic structures, limiting the emissions impact of economic growth and investment. These findings imply that carbon neutrality policies must be tailored to the specific conditions of each country. In countries with higher CO₂ emissions, implementing GEP may yield significant reductions in emissions, highlighting the need for aggressive climate strategies. Conversely, in nations with lower CO₂ levels, enhancing MP and FDI could inadvertently lead to increased emissions, suggesting that careful management is necessary. Overall, a nuanced approach that considers the unique economic and environmental contexts of each country is essential for effectively achieving carbon neutrality. This aligns with the recommendations made by Lee and Zhao ¹²³ and Xing et al., ¹²⁴ who emphasized the importance of developing heterogeneous pathways for emission reduction.

In the fifth model, where CED is the dependent variable, heterogeneous effects have been observed (see Figure 6). MP and REP have stronger positive effects on CED in countries with lower levels of CED compared to those with higher levels. The reason is that, REP policies can catalyse innovation and technology transfer, which are crucial in underdeveloped areas to overcome traditional energy systems. In contrast, regions with established clean energy frameworks may already benefit from economies of scale and advanced technologies, making additional improvements less impactful. Thus, the combination of robust macroeconomic conditions and targeted renewable energy initiatives is essential for fostering clean energy growth where it is most needed. Conversely, the positive effects of EI and FDI are greater in countries with higher CED than in those with lower CED. This is because, the higher CED fosters a more sustainable and stable energy supply, which enhances operational efficiency for businesses, making them more attractive to foreign investors. Additionally, countries that prioritize clean energy often implement progressive policies and technologies that not only reduce environmental impact but also lower operational costs, thereby improving the overall investment climate. Furthermore, these countries are likely to benefit from a skilled workforce trained in clean technologies, which can drive innovation and productivity. In contrast, regions with lower CED (a reverse indicator of CEP) may face higher energy costs and regulatory risks, deterring FDI and limiting the benefits of energy efficiency initiatives. Thus, the synergistic relationship between CED and economic growth creates a more favourable environment for investment and efficiency gains. These prove that strategies to enhance CED should focus on improving MP and renewable energy policies in countries with lower CED levels, as these factors significantly boost development in such contexts. Conversely, for countries with higher CED, fostering energy investment and FDI can further enhance clean energy initiatives. This highlights the need for tailored approaches that consider a country's specific level of CED to effectively promote sustainable energy transitions as also mentioned by Gakuru et al. ²⁹ in green energies countries. These goals align with the current SDG 7, which promotes universal access to clean and modern energy. ⁷

VD and IR technique

In high-income countries, we conduct an estimation of a panel SVAR model that incorporates cross-sectional dependence and is capable of addressing long-term dynamic relationships, following the methodology outlined by Pedroni. ¹⁰⁸ The IR and VD approaches laid out by Lanne and Nyberg ¹⁰⁹ were used to quantify the amount to which FDI, MP, REP, and EI may contribute to CO₂ and CED. The findings of the IR and VD for a 12-year forecasting horizon are shown in Figures 7 to 9. In the situation of high-income countries, the findings urged that 50.145% of the fluctuations in CO₂ can be attributed to unique shocks originating from CO₂ itself, whereas FDI, MP, EI, and REP take part in 22.155%, 20.361%, 3.454%, and 3.885%, respectively. In the other part, the findings pointed out that 45.476% of CED changes can be accounted for by novel shocks originating from CED itself, whereas FDI, MP, EI, and REP account for 21.496%, 22.264%, 3.004%, and 6.760%, respectively. Through a comparison, CO₂ variation caused by GEP (EI and REP) is the least. This implies that EI and REP in support of lowering CO₂ emissions are at a typical level in the countries in question. However, FDI refers to the primary cause of rising CO₂ in high-income countries. These FDI in higher-income countries can contribute to undermining carbon neutrality in several ways including increased emissions from industrial activities and natural resource extraction sectors (e.g. oil, gas, mining, etc.).

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Figure 7.

Variance decomposition (VD) results: (A) represents VD taking CO₂ as a response (dependent) variable and (B) represents VD taking CED as a response (dependent) variable. The results throughout the 12 -year prediction period by considering only the variables of interest. Source: Authors’ illustrations. CED, clean energy development.

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Figure 8.

Impulse response results (CO₂, dependent variable), blue shaded area (confidence intervals: 95%), red line (impulse response function), 12 years’ time horizon. Source: Authors’ illustrations.

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Figure 9.

Impulse response results (CED, dependent variable), blue shaded area (confidence intervals: 95%), red line (impulse response function), 12 years’ time horizon. Source: Authors’ illustrations. CED, clean energy development.

About CED, MP is the first contributor to the reduction in CEP in these countries. This is obvious as increased MP alleviates energy poverty in countries by driving economic growth that enhances household incomes, thereby enabling greater access to energy resources. As economies expand, investments in infrastructure, such as electricity generation and distribution systems, become more feasible, improving energy access for underserved populations. Additionally, a thriving economy fosters job creation and increases government revenues, allowing for targeted policies and subsidies that support renewable energy initiatives and energy efficiency programmes. Consequently, improved macroeconomic conditions not only facilitate the availability of energy but also empower individuals to afford it, ultimately reducing energy poverty and promoting sustainable development. The overall findings reveal how FDI has a strong contribution in increasing CO₂ (22.155%) and also a second contributor in decreasing CEP (21.496%), this confirms hypothesis 5. This highlights the need for actions to reverse the role of FDI in increasing CO₂ emissions, as also suggested by Lee and Zhao. ¹²³

The GEP (EI and REP) strongly contribute to carbon emissions reduction as well moderate reduction in CEP; this affirms hypothesis 4. This illustrates the presence of inequality in reducing carbon emissions and CEP among the higher-income countries in the examined transmission mechanisms (GEP, FDI, and MP). In addition, FDI does not transfer cleaner technologies to the economy and instead worsens carbon neutrality over the long and short terms. As a result, it supports the pollution haven hypothesis' existence in high-income countries. In the same way, MP introduces cleaner technologies into the economy and instead worsens carbon neutrality over the long and short terms. Therefore, the presence of the EKC hypothesis was not also found to be valid in the future due to the absence of inverted U-shaped curve IR of MP to CO₂ in a 12-year forecast horizon, suggesting that strong policies are needed so that an increase in MP could potentially result in decreased carbon emissions in the upcoming years. The structural theory of CEP is confirmed in this study, as improvements in energy systems and economic policies are able to address this kind of poverty. This study brings new insights as it is able to compare the transmission impacts on carbon neutrality and CEP, which can provide an effective solution to the twin energy problems (carbon and poverty). This was neglected by our scholars in literature (for carbon neutrality see Baz et al., ³⁴ Zha et al., ⁶⁹ Meng and Yu ⁷⁰ ; for CEP see Simionescu et al. ⁷⁷ ).

Several studies have suggested that variables like agriculture, urbanization, industrial structure, globalization, economic development, population density, development expenditures, primary energy consumption, and others have an important impact on CO₂ emissions^125–128 and energy prices and household size are other factors that play a role in influencing energy poverty.^129,130 However, this study has limitations on these variables due to inaccessible data or various values being missing. Other limitations include using proxy indicators due to the unavailability of direct measurements, and the study did not account for lower- and middle-income countries. This study also faced additional limitations, such as examining only 77 high-income nations and not taking into account lower- and middle-income countries; some variables utilized proxy indicators that were correlated with the targeted phenomenon, as direct measurements were not accessible due to data constraints. The final limitation is that the study utilized only six macroeconomic variables (GDP, employment rate, inflation, exchange rate, interest rate, and trade intensity) to quantify the MPI, which could also incorporate environmental factors, as suggested by Gakuru and Yang. ¹²²

Conclusion

This research offers valuable insights into achieving clean energy accessibility within a carbon neutrality framework by emphasizing the roles of green energy development, MP, and targeted FDI policy interventions. Addressing these dual energy challenges necessitates coordinated global efforts to ensure sustainable development. However, there was limited understanding of how policies related to clean energy promotion and carbon neutrality influence progress towards these goals. Given the current fluctuations in MP, this study introduces a novel MPI as a mediating variable in the nexus between GEP and FDI. In addition to analysing the effects and mechanisms of GEP and FDI, the study also compares their heterogeneous impacts on achieving carbon neutrality and alleviating CEP.

This paper presents the following key conclusions: (1) GEP can significantly enhance FDI inflows, with greater effects of REP compared with EI. (2) MP mediates the GEP–FDI inflows nexus, with greater positive effects in higher FDI inflows countries. (3) In discussing the heterogeneous transmissions of GEP, MP, and FDI towards carbon neutrality and CEP, FDI contributes more to undermining carbon neutrality compared to the undermining impacts of MP and the favouring effects of GEP. In addition, MP contributes more to reducing CEP than FDI and GEP. (4) A deep analysis of transmission heterogeneity revealed that FDI has strong effects in countries with higher carbon emissions and higher clean energy accessibility countries. Then, MP and REP have strong effects in lower carbon emission countries and lower clean energy accessibility countries. These findings remain robust against endogeneity concerns, demonstrating consistency even when alternative econometric approaches, additional control variables, and substitutions of explanatory variables are applied. The study proved the presence of the pollution haven hypothesis in the high-income countries, using the novel MPI, the presence of the EKC hypothesis was also not found to be valid in the future due to the 12-year forecast period results and due to the below policy implications, the structural theory of poverty is proven in the context of CEP in the higher-income nations.

Policy recommendations

Building on the above conclusions, the following recommendations are proposed. First, given the significant positive mediating role of MP in GEP and FDI nexus, governments should strengthen macroeconomic fundamentals for greater impact. This should be implemented through fiscal discipline, stable exchange rates, and robust infrastructure can amplify the positive nexus between GEP and FDI. This can be achieved by prioritizing investments in human capital, renewable energy research, and regulatory reforms to build a resilient economic environment that supports clean energy growth. Second, given the significant positive contributions of GEP and FDI to MP and CED (fall in CEP) respectively, governments should create policy frameworks that actively integrate green energy incentives with measures to attract FDI. This includes offering tax breaks or subsidies to foreign investors who contribute to renewable energy projects or adopt energy-efficient technologies. Aligning FDI policies with environmental goals ensures sustainable economic growth and fosters innovation in clean energy sectors. Third, given FDI's negative impact on carbon neutrality, governments should implement strict environmental regulations for FDI to limit its contribution to carbon emissions. This includes enforcing carbon pricing, emissions caps, or mandatory adoption of clean technologies by foreign investors. Encouraging green FDI through environmental certification schemes can help reduce the carbon footprint of foreign investments. Fourth, given the contributing role of GEP in alleviating CEP, governments should expand GEP initiatives to address CEP. Here, governments should focus on expanding access to affordable renewable energy in underserved areas. This could involve decentralized solutions such as solar microgrids, targeted subsidies for low-income households, and public–private partnerships to increase clean energy infrastructure. Fifth, given the beneficial effects of GEP, MP, and FDI on CED (a reverse indicator of CEP), as well as the positive impacts of MP and FDI on CO₂ emissions, governments should promote additional GEP related to FDI inflows. This includes encouraging innovations in the green digital economy-based FDI. These include encouraging FDIs highly promoting artificial intelligence and Internet of Things innovations for boosting their economy by optimizing resource use, enhancing productivity. This could be simultaneously controlling carbon emissions through real-time monitoring, predictive analytics, and efficient energy management. Hence, addressing CEP reduction while simultaneously managing carbon emissions in high-income countries requires a balanced approach that encourages sustainable economic growth while mitigating environmental impact. As a matter of fact, transition assistance for affected industries is vital. These aspirations are in alignment with the current SDG 17, which underscores the necessity of global cooperation; SDG 7, which advocates for universal access to clean and modern energy; and SDG 13, which emphasizes the necessity of “taking urgent actions to combat climate change and its impact’.

Limitations and avenues for future research

Taking into account the study's limitations and findings, several directions for upcoming research are available. First, when analysing the GEP–FDI relationship in the nations under investigation, one should focus on spatial and spillover impacts. Likewise, more research can be undertaken within regions, as well as through cross-country comparative studies or case studies, to validate these findings. Second, due to the potential for omitted variable bias, it is advisable to utilize additional factors that contribute to FDI, CO₂ emissions, and CEP, such as gross market size, legal framework, technological advancement, level of privatization, technological innovation, political stability, and others. Third, the study utilized only six macroeconomic variables (GDP, employment rate, inflation, exchange rate, interest rate, and trade intensity) to quantify the novel MPI, which should also be improved to incorporate environmental factors in future research. Fourth, given our preliminary findings that the GEP–FDI relationship is, at the very least, partially mediated by MPI, it is imperative to investigate all possible paths through which GEP could influence FDI in order to shed further light on the FDI development in high-income nations. Fifth, although panel SVAR models attempt to address endogeneity in transmission analysis, they have limited capacity to handle nonlinear dynamics. Future researchers could employ machine learning-based approaches to analyse nonlinear interactions within heterogeneous transmissions to carbon neutrality and CEP. Both ongoing and future studies that address these limitations have the potential to significantly improve countries’ MP, enhance environmental sustainability, and optimize clean energy systems.