1. Introduction

Capital Labour (CL) and Inclusive Growth (IG): CL has the potential to bolster IG by enhancing the economic growth prospects, improving income distribution, creating employment opportunities, and facilitating structural transformation within an economy[32]. Nevertheless, CL also confronts challenges relating to factors like investment rates, savings rates, depreciation rates, population growth rates, human capital development, technological advancements, and institutional quality. Consequently, the interconnection between CL and IG relies on the productive and inclusive allocation and utilization of CL resources. TO and IG: TO can contribute to IG by enhancing economic growth, efficiency, innovation, competition, and consumer welfare[33]. However, TO can also have negative effects on income inequality, environmental degradation, social disruption, and policy autonomy. Therefore, the nexus between TO and IG depends on how TO is balanced and complemented with other policies in a fair and inclusive way. EF and IG: EF can affect IG by reflecting the environmental impact and sustainability of human activities[34]. A high EF can indicate a high consumption of natural resources and a high generation of waste that can harm the environment and reduce the opportunities for future generations. Therefore, the nexus between EF and IG depends on how EF is reduced and offset in a responsible and inclusive way. REU and IG: REU can contribute to IG by enhancing energy security, diversification, affordability, access, and environmental protection. However, REU can also face challenges such as natural resource endowment, technological development, cost competitiveness, policy incentives, public awareness, and social acceptance [35]. Therefore, the nexus between REU and IG depends on how REU is promoted and supported in a sustainable and inclusive way. The paper is organized as follows: Section 2 provides a literature review on the concepts of IG and FP and their linkages. Section 3 describes the data sources and methodology used in the empirical analysis. Section 4 presents the results and discussion of the spatial Durbin model. Section 5 concludes with some policy implications and recommendations for enhancing the contribution of FP to IG in Asia.

2. Literature Review

REU can affect IG by influencing the availability and affordability of energy services in an economy[77]. REU can enhance IG by increasing energy supply and security; reducing energy dependence and vulnerability; lowering energy costs and prices; improving energy access and quality; stimulating innovation and technology development; creating jobs and income opportunities; reducing greenhouse gas emissions and environmental impact; improving health and social benefits[78]. However, REU can also face challenges for IG if there are technical, economic, institutional, or social barriers that hinder its development and deployment[79]. REU can hinder IG by facing intermittency or variability; requiring high upfront investment or subsidies; encountering infrastructure or grid constraints; facing policy or regulatory uncertainty or inconsistency; encountering social or cultural resistance or opposition[80,81].

2.1. Details of previous research

Author(s)	Variables	Methodology	Findings
[82]	Inclusive growth	Mixed methods	The agricultural industry in Myanmar has a lot of untapped potential for promoting fair economic growth, but it requires targeted investments, better infrastructure, and long-term planning to address productivity issues and properly use its competitive advantages.
[83]	Fishery Production	Field Surveys	A multidisciplinary approach was used to classify 20 different fishery production systems into 10 different groups based on ecological, economic, social, technological, and political factors, showing the complexity of artisanal fishing in the area and providing useful information for customized management and development strategies.
[84]	Fishery Production system	Grouping Analysis	The “RAPFISH” methodology was used to evaluate 20 fishery production systems off the coast of Pará, Brazil, and three main groups were identified: industrial and semi-industrial fisheries that show economic and social sustainability, large-scale artisanal fisheries that show ecological sustainability, and small-scale artisanal fisheries. Some of the recommendations are reducing industrial fishing activities, implementing licensing quotas, funding research for semi-industrial and large-scale artisanal fisheries, offering financial incentives for small-scale artisanal fisheries, and encouraging stakeholder involvement in decision-making.
[85]	Inclusive growth	Content analysis	While a consensus definition of inclusive growth is still hard to find, it is clear from a review of ADB’s well-founded knowledge products that it is generally understood to mean “growth with equal opportunities” including economic, social, and institutional aspects. Major suggestions emphasize the need of interdisciplinary strategies, such as the encouragement of sustainable economic growth, guaranteeing fair political involvement, and supporting social safety nets and capacity-building initiatives to promote inclusive growth and development.
[86]	Aquaculture production	Case studies	The research emphasizes that while compartmentalization offers a promising strategy for disease management, its successful implementation in aquaculture depends on aligning with the specific production system and disease epidemiology, implying that it may not be universally applicable, and underscores the importance of integrating HACCP principles for effective biosecurity in compartmentalized systems. Moreover, the study explores the valuable role of compartmentalization in addressing and managing aquaculture disease emergencies..
[87]	Agriculture, forestry, and fishing	Case studies, SLR	The study indicates that worker protection in the Agriculture, Forestry, and Fishing (AgFF) sector is considerably limited, with regulatory protections weaker than in other industrial sectors and enforcement being insufficient. The vulnerability of AgFF workers is aggravated by immigration policies, and the sector’s workforce has historically experienced legal “exceptionalism,” resulting in the exclusion of many regulatory protections specifically designed to secure workers in other industries.
[88]	Capital Labour	Mathematical analysis	The study confirms the presence of a unique marginal rate of technological substitution under optimal capital-labor conditions and establishes a practical procedure for finding the optimal capital-labor ratio in any two-factor production function, grounded in microeconomic theory, where the marginal rate of technological substitution is set to one unit, relying on an accurate representation of key enterprise dynamics.
[89]	Trade openness	New endogenous growth model	The study introduces a novel trade openness index and employs a multifaceted approach, revealing that while human and physical capital positively influence long-term economic growth in India, trade openness has a negative long-term impact, with short-term positive effects, and granger causality tests support the existence of trade openness-led and human capital-led growth hypotheses.
[90]	Ecological Footprint	Statistical analysis	The study presents a methodological framework for calculating ecological footprints associated with leisure tourism in the Seychelles, highlighting the environmental impact of air travel, and raises important questions about the potential role of long-distance travel in safeguarding biodiversity, emphasizing the need for sustainable tourism practices.
[91]	Renewable Energy Utilization	Systematic literature review (SLR)	The study provides a comprehensive overview of island energy resources, investigates the current utilization status and development potential of various renewable energy sources for island power grids, and presents advanced technologies and strategies to improve the penetration of renewables, highlighting the increasing importance of sustainable energy solutions for island communities.

3. Materials and Methods

Econometric Methods

Panel Heteroscedasticity Test

Panel Autocorrelation Test

Panel Unit Root (CIPS) Test

Panel Cointegration test of Westerlund

Long-run Estimation Method

If there is long-run cointegration among the variables with short-run adjustment, the OLS estimation results may yield biased parameter estimates and false conclusions[119]. In panel data analysis, error components may encounter various problems like CD, heteroscedasticity, and autocorrelation in the linear regression model[120]. Especially for models in which the number of selected countries is more than the selected time, i.e. (N>T)[121]. In technical terms, serial correlation and heteroscedasticity are also responsible for issues like inflated R2 [122]. As a result, the coefficient estimates using the general regression methods (i.e., pooled OLS, random effect, and fixed effects) may give misleading inferences. To avoid inefficiency and inconsistency, the use of a linear dynamic model is widely observed in panel data analysis to address serial correlation and unobserved heterogeneity [123]. This study applied the Driscoll and Kraay (D/K) standard errors approach proposed by Driscoll and Kraay (1998), a robust standard error estimation method. It is a non-parametric technique that estimates pooled OLS regression models to find coefficients in the panel data (Driscoll & Kraay, 1998). It assumes that heteroskedasticity affects the error structure and correlation between the panel’s cross-sectional units. It is well calibrated because (a) cross-sectional dependence exists, (b) it can handle missing observations, it deals with an econometric problem of heteroscedasticity, and (d) it can be applied to both balanced and unbalanced panels [124]. In this work, a linear regression model was used to apply the Driscoll and Kraay standard errors. First, the explanatory variables and errors were used to calculate the mean values. Second, these averages were used in weighted heteroscedasticity as described by [125] to produce standard residuals that are robust against CD[126].

$$y_{i.t}={x\mathrm{ʹ}}_{i,t}\beta +{\epsilon }_{i,t}i=\mathrm{1,2},\dots \dots ,{\rm N},t=\mathrm{1,2},\dots \dots ,{\rm T}$$

(6)

where y_(i.t) is the dependent variable and x_(i.t) is the independent variable, the initial element of the vector is 1 and the unknown coefficients are displayed with a (K+1) 1 vector., subscripts $i,t$ shows time and countries[127]. Moreover, the term ${\epsilon }_{i,t}$may have econometric issues like CD and heteroscedasticity in the panel so$\beta$ can be consistently estimated by using OLS regression[128].

Table 1. Descriptive Statistics.

Table 1. Descriptive Statistics.

Panel	Mean	Min	Max	Sd. Dev.	Source
Inclusive Growth (ING) (GDP per person employed)
LMYCs	8.715	6.234	9.768	0.456	W.D.I.
UMYCs	11.865	9.245	12.564	0.501
HYCs	12.545	11.231	13.453	0.392
Total Fishery Production (TFP) (metric tonnes)
LMYCs	4.134204	1.80672	4.615417	0.43924	W.D.I.
UMYCs	5.352802	2.649245	5.610105	0.356066
HYCs	6.586513	4.445568	5.60746	0.026144
Aquaculture production (AP) (metric tonnes.)
LMYCs	2.6166512	-10.94238	3.78219	1.261692	W.D.I.
UMYCs	1.169859	-6.25558	4.29876	1.237899
HYCs	0.9107528	-7.198535	6.107207	1.59848
Trade Openness (TOP) (% of GDP.)
LMYCs	99.56	14.564	423.234	65.563	W.D.I.
UMYCs	76.754	13.522	218.543	34.677
HYCs	73.234	0.154	178.354	33.453
Agriculture, forestry, and fishing (AFF (% of GDP.)
LMYCs	1.322114	-3.963077	9.169629	1.40004	W.D.I.
UMYCs	2.24804	-3.962927	7.510115	1.55605
HYCs	1.282795	-4.012442	4.854778	1.386042
Labour (L) in (millions)
LMYCs	12.877	4.742	22.456	5.772	W.D.I.
UMYCs	14.216712	-1.386294	3.65842	0.7304148
HYCs	1.461135	-0.3581045	3.313094	0.5508925
Capital (K) (in USD)
LMYCs	24.592	1.000	70.433	6.612	W.D.I.
UMYCs	25.432	3.011	46.420	4.433
HYCs	21.583	10.064	41.533	5.153
Total ecological footprint (EF)(Global hectares per capita)
LMYCs	1.603	-0.823	3.421	0.664	W.D.I.
UMYCs	2.312	-1.065	3.546	0.654
HYCs	2.543	-0.234	3.213	0.590
Renewable Energy Utilization (RE) (% of total final energy use)
LMYCs	16.751	0.000	82.654	16.152	W.D.I.
UMYCs	25.687	1.263	86.045	18.432
HYCs	44.673	0.015	92.661	28.654

The table gives the statistics of 10 indicators for three income groups from W.D.I. The indicators are ING (GDP per person employed), TFP (total fish catch and aquaculture production in metric tonnes), AP (fish farming production in metric tonnes), TOP (sum of exports and imports as a percentage of GDP), AFF (value added of agriculture, forestry and fishing as a percentage of GDP), L (total number of workers in millions), K (total fixed capital formation in USD), EF (amount of biologically productive land and water required to meet the consumption demands of a population in global hectares per capita) and RE (share of renewable energy sources in total final energy use in percentage). The statistics are mean, min, max and sd. The table shows that: HYCs have the highest mean ING (12.545), TFP (6.586513), K (4.023328) and EF (2.543), and the lowest min TOP (0.154) and AFF (-4.012442). UMYCs have the highest max ING (12.564) and the lowest min EF (-1.065). LMYCs have the highest mean AP (2.6166512) and TOP (99.56), and the lowest min ING (6.234), TFP (1.80672) and AP (-10.94238). The SD of each indicator varies across groups, but generally LMYCs have higher SD than HYCs. LMYCs have lowest SD (0.664) and HYCs have highest SD (0.590) in EF. HYCs have highest mean (44.673) and max (92.661) and LMYCs have lowest mean (16.751) and min (0.000) in RE. HYCs have highest SD (28.654) and LMYCs have lowest SD (16.152) in RE.

Table 2. Results of pre-estimation tests.

Table 2. Results of pre-estimation tests.

Problem	Test	Lower Middle		Upper middle		High Income
		Test.stat.	Prob.	Test.stat.	Prob.	Test.stat.	Prob.
Cross S.D	Breusch & Pagan LM	1254***	0.00	873.6***	0.000	2264***	0.000
	Pesaran LM adj	19.94***	0.000	18.33***	0.000	54.58***	0.000
	Pesaran CD	6.107***	0.000	8.127***	0.000	12.52***	0.000
Slope Heterogeneity	∆	23.671***	0.000	26.940***	0.000	24.025***	0.000
	∆^ adj	28.449***	0.000	32.378***	0.000	31.37***	0.000
Heteroscedasticity Autocorrelation	Modified Wald Breusch-Pagan /Cook-Weisberg Wooldridge	21889.68* 14.06*** 228.93***	0.000 0.000 0.000	11466.28* 7.55*** 12.786***	0.000 0.000 0.000	32884.53* 44.82*** 208.76***	0.000 0.000 0.000

Note: **Significant at 5% and *** Significant at1%.

The second row shows the results of two tests for slope heterogeneity: ∆ and ∆^ adj. Slope heterogeneity means that the slope coefficients of different units are not equal. Both tests have high test statistics and low probabilities for all groups, which means that they reject the null hypothesis of slope homogeneity. This implies that there is significant slope heterogeneity among the units in each group. The third row shows the results of two tests for heteroscedasticity: Modified Wald and Breusch-Pagan /Cook-Weisberg. Heteroscedasticity means that the variance of the error terms is not constant across units or time. Both tests have high test statistics and low probabilities for all groups, which means that they reject the null hypothesis of homoscedasticity. This implies that there is significant heteroscedasticity among the units in each group. The fourth row shows the results of one test for autocorrelation: Wooldridge. Autocorrelation means that the error terms are correlated across time within units. The test has high test statistics and low probabilities for all groups, which means that it rejects the null hypothesis of no autocorrelation. This implies that there is significant autocorrelation among the units in each group[130].

Table 3. Results of CIPS test.

Table 3. Results of CIPS test.

Variables	Lower Middle	Upper Middle	High Income
At level (Intercept & Trend)
lnIG	-2.074	-2.699**	-2.466
lnTFP	-3.166***	-1.813	-2.998***
lnAP	-3.616***	-3.777***	-3.870***
lnTOP	-2.782***	-2.332	-2.051
lnAFP	-1.998	-1.988	-2.164
lnL	-4.153***	-3.648***	-4.010***
LnK LNEF LNRE	-2.015 -3.063*** -2.156***	-1.977 -2.552 2.899	-2.330 -3.457*** 2.443**
At first difference (only with intercept)
lnIG	-3.522 ***	-3.817***	-4.047***
lnTFP	-4.818***	-3.426***	-3.787***
lnAP	-5.286***	-5.595***	-5.726***
lnTOP	-4.365***	-4.094***	-3.775***
lnAFP	-4.239***	-3.968***	-4.504***
lnL	-5.725***	-5.652***	-5.705***
LnK LNEF LNRE	-4.165*** -3.22***	-3.707*** 3.456***	-3.251*** 3.111***

Note: **Significant at 5% and *** Significant at1%.

The findings also highlight distinctions among the three income groups regarding the level of stability. For instance, lnIG demonstrates greater stability in UMYCs compared to both LMYCs and HYCs at the initial level with an intercept and trend, evident through the lower probability value. Similarly, lnTFP is more stable in LMYCs than in UMYCs when examined at the initial level with an intercept and trend, again indicated by the lower probability value[133].

Table 4. Wester-Lund Cointegration Result.

Table 4. Wester-Lund Cointegration Result.

Panel	Variance Ratio
	Statis.	Prob.
LMYCs	3.485**	0.0005
UMYCs	4.283***	0.0000
HYCs	4.223***	0.0000

Note: ***Significant at 1%.

Furthermore, the results underscore variations in the level of stability across the three income groups. For instance, lnIG demonstrates higher stability in UMYCs in comparison to both LMYCs and HYCs when analyzed at the initial level with an intercept and trend, as evidenced by the lower probability value. Similarly, lnTFP exhibits greater stability in LMYCs when compared to UMYCs when examined at the initial level with an intercept and trend, as indicated by the lower probability value[134].

Table 5. Results of Driscoll Kraay’s Standard Errors regression.

Table 5. Results of Driscoll Kraay’s Standard Errors regression.

Variables	High Income			Lower Middle			Upper Middle
	Coff.	Std. Er.	Prob.	Coef.	Std. Er.	Prob.	Coef.	Std. Er.	Prob.
lnIG	0.535***	0.055	0	0.682***	0.053	0	0.736***	0.689	0.001
lnTFP	0.077**	0.015	0.059	0.099	0.006**	0.086	-0.015	0.004	0.128
lnAP	0.232***	0.031	0	0.053**	0.033	0.055	0.222***	0.041	0.008
lnTOP	0.345***	0.09	0.006	0.544***	0.248	0	2	296***0.083	0.002
lnAFP	0.788**	0.004	0.08	-0.889**	0.009	0.017	0.0234***	0.011	0.007
LnRE	-0.345***	0.005	0.03	-0.576***	0.003	0.012	0.896***	0.01	0.005
F-Stat	177.44*** (0.000)			134.29*** (0.000)			81.89***(0.000)
R2	0.566			0.678			0.36

Note: **Significant at 5% and *** Significant at1%.

In the case of UMYCs, the results point to a positive and statistically significant impact on ING stemming from ING itself, TFP, and AP, whereas AFF and RE exhibit a negative and statistically significant influence on ING. In essence, this signifies that the enhancement of inclusive growth, alongside increased fishery, and aquaculture production, can be conducive to fostering inclusive growth within UMYCs. Conversely, an escalation in agricultural and forestry activities, as well as the utilization of renewable energy sources, may hamper inclusive growth within this income group. For HYCs, the investigation uncovers a positive and statistically significant association between ING and AP, while TFP and RE exhibit a negative and statistically significant association with ING. This suggests that augmenting both inclusive growth and aquaculture production can bolster inclusive growth within the high-income category. However, an increase in fishery production and the utilization of renewable energy sources may act as deterrents to inclusive growth in this group. Additionally, the research findings underscore the overall significance and robustness of the regression model, which is substantiated by the elevated F-statistic and R-squared values. Furthermore, the results remain resilient when subjected to a battery of pre-estimation tests aimed at detecting and addressing potential issues like cross-sectional dependence, slope heterogeneity, heteroscedasticity, and autocorrelation. The Westerlund cointegration test is also employed to investigate the existence of a long-run equilibrium relationship among the variables. This research paper makes a notable contribution by furnishing empirical evidence regarding the role of fishery and aquaculture production in the context of fostering inclusive growth in the Asian region. Additionally, the paper spotlights the intricate trade-offs between economic development and environmental sustainability as they relate to trade openness, agriculture, forestry, and fishing, ecological footprint, and renewable energy utilization. The paper concludes by offering policy implications geared towards the enhancement of inclusive growth in Asia, encompassing aspects such as improved management of fishery and aquaculture, diversification of economic activities, reduction of ecological footprint, and an increased focus on renewable energy utilization.

4. Discussion

The findings of this research paper have some important policy implications for enhancing inclusive growth in Asia through improving fishery and aquaculture management, diversifying economic activities, reducing ecological footprint, and increasing renewable energy utilization. First, policy makers should promote sustainable fishery and aquaculture practices that can increase productivity and profitability without compromising environmental quality and social equity. This can be achieved by implementing effective regulations, incentives, and monitoring systems to prevent overfishing, pollution, and conflicts among stakeholders. Secondly, it is imperative for policymakers to promote economic diversification to lessen reliance on the agricultural, forestry, and fishing sectors, which not only offer limited value but also entail significant environmental costs. This objective can be realized through the active support of industrialization, urbanization, and the development of the service sector, all of which have the potential to generate increased employment opportunities and diverse income streams for the population. Thirdly, policymakers must prioritize the reduction of the ecological footprint, as this measure can effectively counteract environmental degradation and the adverse effects of climate change that pose a substantial threat to inclusive growth. Achieving this goal necessitates the implementation of green growth strategies that enhance resource efficiency and environmental preservation through a combination of technological innovation and alterations in behavior. Fourthly, policymakers ought to boost the utilization of renewable energy sources to curtail the consumption of fossil fuels and mitigate the emissions of greenhouse gases, both of which can be detrimental to inclusive growth. This objective can be accomplished by making strategic investments in renewable energy infrastructure and development, providing a clean and cost-effective energy supply for various sectors and activities[155].

5. Conclusions

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