Fishing grounds footprint and economic freedom indexes: Evidence from Asia-Pacific.

Environmental challenges are as wide as the universe so that its different dimensions can be the subject of diverse studies. In this research, using 17 data from Asia-Pacific during the period 2000 to 2017, an attempt has been made to investigate the economic factors responsible for the ecological footprint in the fishing sector. The main contribution of the present study is to investigate the effects of nine economic freedom indicators along with other control variables on environmental pressure on the status of fishery resources. Based on the results, the Kuznets curve hypothesis was confirmed in the fishing grounds footprint, so that the growth of GDP per capita shows a positive and significant effect, while its squared form coefficient is negative. Other control variables including natural resource rents, urbanization, and energy intensity, do not show significant effects on the fishing footprint. The different components of economic freedom show different effects, while their cumulative effects in the form of the total economic freedom index have a positive effect on the footprint of fishing and lead to increased extraction from fishing resources. The results show that the Government Integrity, Tax Burden, Business Freedom, Monetary Freedom indices increase the fishing footprint, while the indices of trade freedom and investment freedom, by revealing the negative effects on the fishing footprint, have beneficial environmental effects in reducing the pressure on the aquatic resources of countries. The results of the present study reveal the need to examine how the various dimensions of economic freedom affect to provide the proper management of fishery resources.

1. Introduction

Seafood plays an essential role in the human food chain and global food security. The demand for seafood has been extended in the last two decades and fish consumption growth is twice the growth of population in the globe [1]. Also, the fisheries sector is considered as a growth engine in many countries and the livelihood of more than 12% of people around the world depend on the fisheries income [2]. Fishes are among the highest traded of all food commodities [3] and in the year 2018, more than 220 states over the world had traded approximately 67 million tons of fish globally which forms 1 percent of the value of total merchandise trade. In addition, on one hand, the pattern of seafood trend shows that fishing activities shifted to the global south as Asian countries and China, Viet Nam, India, and Thailand and other Mekong Delta are a major exporter of fish [4, 5]. On the other hand, since 1961, average per capita fish consumption has been increasing in Asia at an annual rate of 2 percent because of increasing urbanization and the significant increase in the population of emerging economies, and their high proportion of middle-class citizens with higher income. These issues confirm the growing role of Asian countries in fish production and consumption all over the world. Fisheries in Asia-Pacific has a major role in economic earning. For example, more than 2.2 million Indonesian people do economic activities in the fishing sector [5]. In the Philippines, the fisheries sector provided direct and indirect employment to over one million people [6], 80% of people livelihoods in the Lower Mekong River Basin depend on the river and its rich natural resources also the income of more than 120 million habitants in the coral triangle—Amazon of seas- depends on marine and coastal resources.

Although, according to the FAO report in the year 2020, about 31 percent of fishing grounds are experiencing overfishing and 58 percent of fish stocks are fully misused. Industrial fishing, such as cyanide and dynamite fishing is now thousands of miles underwater. The creatures of the ocean floor are very long-aged and grow very slowly, which means that they are extremely vulnerable to overfishing. The establishment of marine deserts is a new phenomenon that occurs in some parts of the world seas and oceans. In these barrens, a part of the sea that has been plowed by trawl nets becomes lifeless and loses its animal diversity in the bottom and middle floor of seas, and fishing activities become generally lean in these areas [7]. In this context, despite Asia and the Pacific have had valuable natural aquatic capital, the new reports proclaim that the deterioration rate of biodiversity especially the ocean’s ecosystem in this region is twice the global average [6]. In addition, population and consumption in the Asia and Pacific region have increased rapidly and the pressures on the environment are faster than the rapid rates of economic growth [8]. Underwater bio capacity growth is slower than population growth in Asia and the Pacific region, and this region faces a bio capacity deficit that could cause social and economic problems and threaten food security and welfare in the region [9].

This information’s warnings are of great environmental concerns on world fish stocks and many researchers consider the mankind-driven pressures on the water ecosystems as the main contributor to this problem and they conclude the Kuznets curve which explains how economic growth leads to environmental deterioration. Regarding aquatic food systems, past research that draws from the human ecology framework indicates positive associations between economic growth and declines in aquatic biodiversity, and increases in fishery footprint [10, 11].Authors in [12-14] showed that developed countries tend to increase their aggregate consumption of environmental space by procuring favorable trade relations with less powerful, export-oriented countries. Periphery regions and countries also, oftentimes, become sinks for various forms of pollution. McMichael in [15] emphasizes that, along with the globalization of industry and textiles, capitalist globalization expanded into the agri-food systems of less-affluent nations over the last several decades that depleted the fish reserves in developing countries. Longo et al. in [16] demonstrate that economic growth and incorporation into the global food system are positively [16] associated with increases in ecologically intensive aquaculture. Likewise, Clark and Longo in [3] examined the effect of economic development, region, and period on fisheries footprints. The results indicated that the magnitude of fisheries footprints in the recent decade is higher than in past decades. In addition, they showed that economic development significantly affects fisheries footprints in less developed countries more than in developed countries. Further, Solarin et al., in [17] observed the persistency and sustainability of fishing grounds footprints using a comparative analysis in 89 countries and they showed that fisheries footprint shows a sustainable and persistent pattern in low-income countries.

There is an impartially strong consensus that economic freedom is positively correlated with economic growth. Economic freedom brings economic growth, and welfare, however, it stimulates demand for food, safe water, energy, roads, railways, urban lands, agricultural lands, coastal development, and other infrastructures. Then economic freedom is often mentioned as a crucial component influencing more resource-intensive activities and government and private sector activities increase the ecological footprint and consequently raise fishing ground footprint [18]. For example, the leakage of toxic material from agricultural and urban wastes to freshwater natural flow led to lower agricultural productivity and freshwater fish stocks. Overfishing, water pollution, biodiversity loss caused by extended economical activities, threatens the sustainability of the marine system [19]. However, a review of the theoretical foundations suggests that economic freedom from various other dimensions can also have positive or negative effects on the environmental situation. In the light of these foundations, this research is interested in how different economic freedom variables affect fishing ground footprints in Asia and the Pacific. Also, the present study applies a spatial econometric model to consider the spatial features of fishing ground footprints in the region and to see whether destructive fishing activities do permeate to neighboring countries or not?

The rest of this research is structured as follows: section 2 includes a literature review, methodological approach of this research and data are presented in section 3, empirical results are discussed in section 4 whereas, section 5 conclude this research with policy implications.

2. Methodological approach and data

2.1. Methodological approach

2.1.1. Model specification

We applied the spatial econometric model to analyze the fisheries footprints of financial freedom, keeping in mind the spatial dependency of the ecological footprints of determinants in the Asia-Pacific countries. Following the suggestions of [16] and [20], we can write the research model of the ecological footprint as:

The logarithm of the ecological footprint per capita index () is a function of some explanatory variables, including the logarithms of GDP per capita (), energy intensity (), trade openness (), urbanization (), natural resource rents (), and the economic freedom indexes (). The principle of Environmental Kuznets (EKC) continues to be a criterion for indicating how environmental sustainability is inspired via economic development. Keeping in mind, the environmental quality is an inverted U-Shaped whilst we regard monetary development as an impartial variable. be it as it may, the environmental popular first decreases and then will increase because the economic system expands [21]. Accordingly, inside the ecological footprints equation, the insignificant coefficient of the squared form of GDP according to capita is technically mentioned and wishes to be studied. Tsuchiya et al., in [22] also showed that urbanization, population, and aging have a significant opposing role in food ecological footprint per capita in Japan.

As economic freedom motivates a high rate of resources waste, then many philosophers in the majority of studies had supported the idea of contrary effects of economic freedom on ecological footprints through three major channels of efficiency channels, trade regulations, and stability effects. According to the efficiency channel, on one hand, they expect that economic freedom make markets efficient and competitive, then resources will be used less and more efficiently, on the other hand, they notice that efficient markets bring regulations, especially ecological regulations in which consumers face clean production of goods and services, the economic freedom could decrease the ecological footprints. Likewise, researchers refer trade regulation channel to the "pollution heaven" concept in which trade liberalization encourages countries with a high level of capital-intensive industries to specialized in dirty industries. Similarly, according to stability effects, economic freedom could make stable the macroeconomic environment in the long run, and investors could safely make their investment decisions, and if the composition of industries in that countries is more resource-based, then the ecological footprints may raise [23]. In this context, Clarck and Lango in [3] more specifically explain the channels in which economic freedom causes fisheries footprints and show how economic freedom and human activities damage the marine system and threaten the food security of nations, especially in coastal regions. The World Wide Fund for nature living planet report 2012 (WWF 2012) also identified the following major direct pressures by economic activities on natural resources as seas and oceans which is given below as;

Economic freedom, and consequently rapid economic development increase need for more energy, water, materials, roads and railways, coastal developments, agricultural lands, urban lands, industrial estates, and touristic places, therefore coastal areas fall in danger of land-use changes, ruining, and fragmenting by human activities which cause coastal habitat loss and damaging fishing activities.

The high level of income growth and population growth resulted from economic freedom stimulates resource conversion to goods and services and expands demand for seafood internationally then overfishing phenomenon and destructive fishing as cyanide and dynamite fishing turn up in order to reply world demand for seafood.

Industrial and metropolitan area’s sullage pollution and overuse of agricultural pesticides and chemical fertilizers cause freshwater pollution. Freshwater pollution leads to the loss of aquatic ecosystems and food, particularly fish, and to negative economic impacts, such as high treatment costs to turn polluted water into drinking water. The treadmill of production scholars also indicates that economic freedom increases environment demolition [6, 24] and interrupt the cycle of ecosystem especially in aquatic food systems [25-28].

Emission of greenhouse gasses arises from industrial parks led to climate changes that are harmful to sea and oceans ecosystem and biodiversity [29, 30].

A capitalist world food system which is more likely to be ecologically intensive [16, 31] caused developed and richer countries tend to transfer fisheries burden on less developed countries by importing fish and luxury sea foods from less developed countries, this cause these poor nations try to modernize and commercialize their fishing sector and do overfishing from sea resources that bring to lose fish stocks in the long run, such that the poorer coastal countries will be more dependent upon imports for feeding their population in the future [20, 32].

Regulations, allotments, monitoring, and fishing restrictions in developed countries that are consequences of economic freedom, curbs access to inland marine natural resources of developed countries and they shift the fishing burden on areas of the global South areas as in Asia [4, 5].

Economic freedom developed transportation and longtime storage technologies, and expanded supply chain and widened consumer choices as well the regions with limited or no access to seafood, can demand and have access to a variety of seafood that is produced far from their own countries, even in low prices. Then the pressure on global available fish stocks increases and fish stocks would be more vulnerable to annihilation.

Urbanization is one of the consequences of economic freedom, promptly increase citizen’s demand for fast foods such as fish products to eat far from home in cities, in addition, healthy living styles and dietary preferences in urban areas has intensified global demand for fish products and the pressure on fish stocks has been increased that could have more adverse effects on fisheries footprints, especially in emerging markets.

2.1.2. Spatial econometric model

Following the suggestion of Anselin et al., in [33], a spatial panel model follow a spatially-autoregressive process in the error term. LeSage and Pace in [34] outlined the spatial Durbin model, which includes spatially-lagged independent variables. This research employed the spatial lag model, spatial error model, and spatial Durbin model which are mathematically expressed as follows:

In which represents a dependent variable for cross-sectional unit i = 1, 2, …N at time t = 1, 2, …. . Similarly, stands for a 1×K vector of exogenous variables, while represents a K×1 vector of parameters. It should be noted that accounts for the interaction effects of dependent variables in the adjacent units on the dependent one, denotes element i, j of an N×N matrix of spatial weights, denotes the endogenous interaction effect response parameter, stands for an error term of independent and identical distribution, is a spatial particular effect, and accounts for the time-period particular effect. A spatial particular effect controls for the entire time-invariant space-specific variables the exclusion of which would yield biased estimations in a usual cross-sectional work. On the other hand, a time-period specific effect controls for the entire time-specific effects the exclusion of which might produce biased estimations in common time-series research [35]. The error term of unit i in the spatial error model in Eq (3) (i.e., ) is considered to be dependent on the error terms of adjacent units j based on matrix W and an idiosyncratic component . Furthermore, the spatial Durbin model in Eq (4) was recommended to be employed by [34]. It would extend the spatial lag model with independent variables of spatial lagging where is a vector of parameters.

2.1.3. The long-run estimation using PFMOLS model

According to [36, 37], the OLS estimator is a biased and inconsistent estimator when applied to cointegrated panels. This led to the development of a new model known as the Panel Fully Modified OLS (FMOLS) estimator. The PFMOLS estimator has the advantage of generating consistent estimates of in relatively small samples while controlling for the possible endogeneity of the regressor and their consequent serial correlation, thereby allowing for standard normal inference [38]. Also, PFMOLS can accommodate considerable heterogeneity across the members of the panel (i.e., countries in this case).

As another advantage, the cointegrated panel approach under PFMOLS enables researchers to selectively pool the long-run information contained in the panel while permitting the short-run dynamics and fixed effects to be heterogeneous among different members of the panel. Last, in addition to producing asymptotically unbiased estimators, the PFMOLS methodology produces nuisance parameter-free standard normal distributions.

2.2. Data

The data are collected form 17 countries in Asia-Pacific from 2000 to 2017. A description of the constructed variables used in the analysis is presented in Table 1. Furthermore, Table 2 provides the summary statistics of the data. Data used in this research are taken from World Development Indicator (WDI), Global Footprint Network (GFN), and The Heritage Foundation. All variables are in a logarithmic form and, therefore, the estimated coefficients are elasticities.

Table 1

Description of variables construction.

Variable	Variable constructed	Source
lnFGF_it	lnFGF_it=log(FGF_it)	GFN
	FGF_it = Fishing Grounds footprint in the country i in time t
lnGDP_it	lnGDPP_it=log(GDPP_it)	WDI
	GDP_it = GDP per capita in 2010 prices$
lnENER_it	lENER_it=log(ENER_it)	WDI
	ENER_it = Energy intensity level of primary energy
lnURB_it	lnURB_it=log(URB_it)	WDI
	URB_it = Urban population (as a percentage of the total population)
lnRENT_it	lnRENT_it=log(RENT_it)	WDI
	RENT_it = Total natural resources rents (as a percentage of GDP)
lnEFI_it	lnEFI_it=log(EFI_it)	Heritage
	EFI_it = Total Economic Freedom Index
lnPRI_it	lnPRI_it=log(PRI_it)	Heritage
	PRI_it = Property Rights Index
lnGII_it	lnGII_it=log(GII_it)	Heritage
	GII_it = Government Integrity Index
lnTBI_it	lnTBI_it=log(TBI_it)	Heritage
	TBI_it = Tax Burden Index
lnGSI_it	lnGSI_it=log(GSI_it)	Heritage
	GSI_it = Government Spending Index
lnBFI_it	lnBFI_it=log(BFI_it)	Heritage
	BFI_it = Business Freedom Index
lnMFI_it	lnMFI_it=log(MFI_it)	Heritage
	MFI_it = Monetary Freedom Index
lnTFI_it	lnTFI_it=log(TFI_it)	Heritage
	TFI_it = Trade Freedom Index
lnIFI_it	lnIFI_it=log(IFI_it)	Heritage
	IFI_it = Investment Freedom Index
lnFFI_it	lnFFI_it=log(FFI_it)	Heritage
	FFI_it = Financial Freedom Index

Table 2

Summary statistics over the years 2000–2017.

Variable	Mean	Median	Maximum	Minimum	Std. Dev.	Observations
lnEFP_it	1.988	5.419	4.00	-2.66	1.68	309
lnGDP_it	8.389	8.120	10.957	6.121	1.443	309
lnENER_it	1.660	1.685	2.533	0.657	0.384	309
lnURB_it	3.840	3.840	4.605	2.595	0.539	309
lnRENT_it	0.060	0.840	3.819	-8.075	2.802	309
lnEFI_it	4.091	4.044	4.493	3.777	0.170	309
lnPRI_it	3.743	3.912	4.500	2.303	0.553	309
lnGII_it	3.548	3.466	4.543	1.386	0.531	309
lnTBI_it	4.320	4.329	4.517	3.987	0.114	309
lnGSI_it	4.359	4.419	4.557	3.666	0.177	309
lnBFI_it	4.173	4.217	4.605	3.570	0.238	309
lnMFI_it	4.322	4.331	4.546	3.661	0.117	309
lnTFI_it	4.216	4.285	4.500	2.976	0.240	309
lnIFI_it	3.726	3.807	4.500	1.609	0.495	309
lnFFI_it	3.763	3.689	4.500	2.303	0.373	309

Economic freedom index characterizes the amount of people’s/organizations associations around the world and recently has an upward trend and the international economy experiences a moderately free situation [39]. The Heritage Foundation applies 4 major categories: Rule of Law (property rights, judicial effectiveness, government integrity), government size (tax burden, government spending, and fiscal health), regulatory efficiency (business freedom, labor freedom, and monetary freedom), and market openness (trade freedom, investment freedom, and fiscal freedom) to measure economic freedom index. In this research, the time span of some freedom indexes are defined as:

Property rights:

Indicates the ability to build up non-public belongings and wealth.

Authority’s integrity:

It suggests the government practices in opposition to the systemic corruption of government establishments as bribery, nepotism, cronyism, patronage, embezzlement, and graft.

Tax burden:

Suggests the economic burdens (as direct taxes and indirect taxes consisting of payroll, income, and excise taxes, in addition to price lists and cost-delivered tax) impose on financial sports by governments.

Authorities spending:

It indicates the fee, length, and invasiveness of government.

Commercial enterprise freedom:

Shows the individual’s ability to set up and run a company without undue interference from the state.

Financial freedom:

Financial freedom combines a degree of charge balance with an evaluation of price controls.

Exchange freedom:

Alternate freedom is a composite measure of the absence of tariff and non-tariff obstacles that have an effect on imports and exports of products and services.

Funding freedom:

Funding freedom indicates that there are no constraints on the float of funding capital. individuals and corporations are allowed to transport their resources into and out of particular activities.

Financial freedom:

Financial freedom way that people get to make lifestyles decisions without being overly confused about the monetary effect because they may be supported.

The summary statistics of the data are giving in Table 2 which shows that for most of the variables, the standard deviations are significantly lower than the mean, showing a low level of fluctuations in the model’s variables.

3. Experimental results and discussion

3.1. Spatial econometric model

In directive to investigate the probability of the time-period fixed effects and spatial fixed effects in the model, two separate likelihood ratio (LR) tests were used. For this purpose, the model with simultaneous spatial and time-period fixed effects is compared to the model of time-period fixed effects and/or the model of spatial fixed effects. If the null hypothesis is rejected, the model with simultaneous spatial and time-period fixed effects is selected. On the other hand, if the null hypothesis is accepted, the subsequent model is selected. LR test statistics for the models are presented in Table 3. The test results indicate the significance of the LR test statistics and the rejection of the null hypothesis for only the time-period fixed effects in most models. Therefore, the spatial fixed effects are selected as the best model to proceed with estimation. Table 3 represents the Hausman test results to examine the possibility of replacing the fixed-effect model with a random-effect model. The null hypothesis in this test emphasizes the existence of random effects in the model. The results of the Hausman test reject the assumption of random effects in the spatial lag model for all the models and confirm the existence of fixed effects at a significance level of 1%.

Table 3

The LR test for the existence of the spatial and time-specific fixed-effect in the models and Hausman test results.

	Spatial fixed effects		Time-Specific fixed effects		Hausman test-statistic
Model 1	5.973	(0.996)	891.818	(0.000***)	55.498	(0.000***)
Model 2	8.865	(0.963)	854.576	(0.000***)	64.43	(0.000***)
Model 3	6.311	(0.995)	863.19	(0.000***)	50.683	(0.000***)
Model 4	6.079	(0.996)	870.599	(0.000***)	61.984	(0.000***)
Model 5	9.446	(0.949)	913.701	(0.000***)	72.258	(0.000***)
Model 6	6.006	(0.996)	828.285	(0.000***)	50.463	(0.000***)
Model 7	11.9	(0.852)	896.243	(0.000***)	60.974	(0.000***)
Model 8	11.073	(0.891)	903.612	(0.000***)	62.55	(0.000***)
Model 9	5.462	(0.998)	892.453	(0.000***)	67.669	(0.000***)
Model 10	8.757	(0.965)	894.828	(0.000***)	34.094	(0.000***)
Model 11	5.944	(0.996)	860.759	(0.000***)	53.034	(0.000***)

Note: p-value

***, **, and * show significance at 1%, 5%, and 10% level respectively.

Source: Authors’ estimations.

Another test in Table 4 is used to examine whether the inclusion of the spatial lag or spatial error in the model in the absence of spatial interaction effects would lead to a significant improvement in the model. Following the suggestion of [22] using the residuals of a non-spatial model, LM tests are applied to a spatially-lagged dependent variable and spatial error autoregressive. The test statistic has the chi-square distribution. If the null hypothesis of the LM test is rejected, the presence of the spatial lagged model and the spatial error model is confirmed. Since the LR test results confirmed the existence of the spatial fixed effects, this study examines only the Lagrange Multiplier (LM) statistics for this model. The results presented in Table 4 suggest that the test statistic values in all models are significant at the level of one percent. Therefore, spatial lagged and spatial error effects must be ignored in the model. The lack of presence of spatial correlation effects in the model emphasizes the need not to consider such effects when investigating the factors affecting fishing grounds footprints in experimental studies.

Table 4

The LM test for the existence of the spatial lag or the spatial error in the models.

		Spatial fixed effects		Time-period fixed effects		Spatial and time-period fixed effects
Model 1	LM spatial lag	0.331	(0.565)	0.645	(0.422)	0.135	(0.713)
	LM spatial error	0.137	(0.712)	0.31	(0.577)	14.663	(0.000***)
Model 2	LM spatial lag	5.421	(0.02**)	1.153	(0.283)	9.762	(0.002***)
	LM spatial error	23.71	(0.000***)	0.34	(0.56)	0.202	(0.653)
Model 3	LM spatial lag	0.476	(0.49)	0.748	(0.387)	0.312	(0.577)
	LM spatial error	3.37	(0.066*)	0.53	(0.466)	13.957	(0.000***)
Model 4	LM spatial lag	1.433	(0.231)	0.82	(0.365)	1.685	(0.194)
	LM spatial error	7.65	(0.006***)	0.188	(0.664)	4.002	(0.045**)
Model 5	LM spatial lag	0.159	(0.69)	0.967	(0.326)	0.151	(0.698)
	LM spatial error	0.163	(0.686)	1.056	(0.304)	14.373	(0.000***)
Model 6	LM spatial lag	2.828	(0.093*)	0.648	(0.421)	4.79	(0.029**)
	LM spatial error	1.409	(0.235)	0.317	(0.573)	27.863	(0.000***)
Model 7	LM spatial lag	0.294	(0.588)	1.46	(0.227)	0.127	(0.721)
	LM spatial error	0.1	(0.752)	0.565	(0.452)	12.34	(0.000***)
Model 8	LM spatial lag	0.224	(0.636)	0.508	(0.476)	0.404	(0.525)
	LM spatial error	0.024	(0.876)	0.033	(0.857)	12.036	(0.001***)
Model 9	LM spatial lag	4.876	(0.027**)	0.161	(0.688)	2.902	(0.088*)
	LM spatial error	0.026	(0.872)	1.004	(0.316)	9.773	(0.002***)
Model 10	LM spatial lag	0.338	(0.561)	1.063	(0.303)	0.055	(0.815)
	LM spatial error	1.387	(0.239)	0.02	(0.888)	9.948	(0.002***)
Model 11	LM spatial lag	5.065	(0.024**)	0.638	(0.425)	5.277	(0.022**)
	LM spatial error	11.203	(0.001***)	0.285	(0.594)	0.973	(0.324)

Note: p-value

***, **, and * show significance at 1%, 5%, and 10% level respectively.

Source: Authors’ estimations.

Tables 5 and 6 present the results of estimating different models. The control variables of all 11 estimation models are the same and the difference is in the existence of 9 different components of economic freedom in each model. The reason for such a distinction is the possibility of collinearity between different indicators of economic freedom. According to the results, each percent growth in GDP per capita leads to significant growth of about 3.6 percent in the fishing footprint index. The coefficient of the squared form of the logarithm of GDP per capita is also negative and significant. A value of -1.9 means that the Kuznets hypothesis holds for fishing grounds and an inverted U-shape when we regard economic. This result is in accordance with [3, 22, 29, 31] results where they conclude that the effects of GDP per capita on fisheries footprint depend on the income level and they pursue economic development that damage the quality of the environment, where developed countries focus on activities that are more detrimental to the environment. Other control variables, including the logarithm of energy intensity as an indicator of energy efficiency, rent of resources, and urbanization, do not show significant effects on the Fishing Grounds footprint, this result is inconsistent with [39].

Table 5

Estimation results of PFMOLS Model 1–6 (when the variable included are stationary).

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
lnGDP_it	3.638	3.461	3.647	3.835	3.482	3.629
	(0.000***)	(0.000***)	(0.000***)	(0.000***)	(0.000***)	(0.000***)
lnGDPit2	-0.188	-0.177	-0.19	-0.192	-0.189	-0.187
	(0.000***)	(0.000***)	(0.000***)	(0.000***)	(0.000***)	(0.000***)
lnENER_it	0.175	0.164	0.214	0.248	0.116	0.177
	(0.316)	(0.342)	(0.228)	(0.155)	(0.497)	(0.315)
lnRENT_it	-0.005	0.01	-0.016	0.012	0.001	-0.005
	(0.873)	(0.745)	(0.63)	(0.692)	(0.964)	(0.878)
lnURB_it	-0.355	-0.416	-0.382	-0.733	-0.173	-0.354
	(0.236)	(0.16)	(0.203)	(0.024**)	(0.556)	(0.239)
lnEFI_it		0.991
		(0.002***)
lnPRIit			-0.1
			(0.237)
lnGII_it				0.201
				(0.004***)
lnTBI_it					0.912
					(0.000***)
lnGSI_it						0.016
						(0.929)
LogL	47.178	51.978	47.889	51.292	56.398	47.182
R^2	0.985	0.985	0.985	0.985	0.986	0.985

Note: p-value

***, **, and * show significance at 1%, 5%, and 10% level respectively.

Source: Authors’ estimations.

Table 6

Estimation results of PFMOLS Model 7–11 (when the variable included are stationary).

	Model 7	Model 8	Model 9	Model 10	Model 11
lnGDP_it	3.643	3.621	4.415	3.22	3.687
	(0.000***)	(0.000***)	(0.000***)	(0.000***)	(0.000***)
lnGDPit2	-0.192	-0.185	-0.226	-0.163	-0.191
	(0.000***)	(0.000***)	(0.000***)	(0.000***)	(0.000***)
lnENER_it	0.107	0.213	0.165	0.226	0.175
	(0.54)	(0.219)	(0.331)	(0.185)	(0.316)
lnRENT_it	-0.014	0.007	-0.001	-0.014	-0.004
	(0.652)	(0.825)	(0.984)	(0.649)	(0.901)
lnURB_it	-0.407	-0.262	-0.364	-0.424	-0.373
	(0.17)	(0.381)	(0.213)	(0.147)	(0.217)
lnBFI_it	0.318
	(0.005***)
lnMFI_it		0.462
		(0.008***)
lnTFI_it			-0.402
			(0.000***)
lnIFI_it				-0.179
				(0.000***)
lnFFI_it					0.038
					(0.61)
LogL	51.161	50.781	55.353	55.173	47.311
R^2	0.985	0.985	0.986	0.986	0.985

Note: p-value

***, **, and * show significance at 1%, 5%, and 10% level respectively.

Source: Authors’ estimations.

The total economic freedom index in Model 2A shows a positive and significant effect so that each percentage increase leads to a 0.991% increase in the Fishing Grounds footprint. this result is inconsistent with the result of [21], where they confirm the positive effect of economic freedom on ecological footprints. However, the different components of the economic freedom index show different effects, so that the Government Integrity, Tax Burden, Business Freedom, Monetary Freedom Indices, respectively with coefficients of 0.2, 0.912, 0.318, and 0.462, have positive and significant effects on the dependent variable, these results are inconsistent with [35, 40] while Trade Freedom index with a coefficient of -0.402 and Investment Freedom index with a coefficient of 0.179 showed negative and significant effects on the Fishing Grounds footprint and this result is in according to the result of [21], and [41]. Although many researchers confirm the positive effects of economic freedom on economic growth and welfare, however, they argue that economic freedom stimulates demand for food, safe water, energy, roads, railways, urban lands, agricultural lands, coastal development, and other infrastructures. then government and private sector economic activities increase ecological footprint [18]. Moreover, economic freedom motivates a high waste rate of resources, then many theorists in the bulk of studies had supported the idea of the adverse effects of economic freedom on ecological footprints through three major channels of efficiency channels, trade regulations, and stability effects. Such conflicting results for different indexes of economic freedom were also confirmed in this study.

3.1. The Panel Fully Modified OLS (PFMOLS) model

Following the suggestion of [42], we employed the panel fully modified OLS model in fishing grounds footprint using panel co-integrating estimators, for more accurate investigation. We also observe the short and long-run effects of the variables through this model. The possibility of non-stationarity variables and the risk of a long-run relationship led to the re-estimation of all the models in Tables 7 and 8 using a different econometric model that allows the long-term effects of the variables to be examined. Comparing the results of Table 5 and with that of Tables 7 and 8.We observed that the sign of the coefficients of Tables 7 and 8 are the same as in Tables 5 and 6, and only the magnitude of the coefficients is slightly different. Therefore, the results can be viewed with more confidence.

Table 7

Long-run estimation results of PFMOLS Models (when the variables are non-stationary).

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
lnGDP_it	5.079	4.707	6.793	5.232	4.753	5.155
	(0.000***)	(0.000***)	(0.000***)	(0.000***)	(0.000***)	(0.000***)
lnGDPit2	-0.269	-0.247	-0.373	-0.268	-0.265	-0.275
	(0.000***)	(0.000***)	(0.000***)	(0.000***)	(0.000***)	(0.000***)
lnENER_it	0.461	0.422	0.428	0.565	0.287	0.417
	(0.173)	(0.201)	(0.31)	(0.092*)	(0.38)	(0.217)
lnRENT_it	-0.019	-0.004	-0.041	0	-0.014	-0.021
	(0.713)	(0.939)	(0.559)	(0.993)	(0.784)	(0.682)
lnURB_it	-0.551	-0.575	-0.865	-1.044	-0.297	-0.536
	(0.294)	(0.263)	(0.153)	(0.064*)	(0.56)	(0.305)
lnEFI_it		1.023
		(0.051*)
lnPRI_it			-0.179
			(0.309)
lnGII_it				0.228
				(0.045**)
lnTBI_it					0.976
					(0.005***)
lnGSI_it						-0.124
						(0.672)
R^2	0.987	0.987	0.988	0.987	0.987	0.986

Note: p-value

***, **, and * show significance at 1%, 5%, and 10% level respectively.

Source: Authors’ estimations.

Table 8

Estimation of PFMOLS Model for long run effect when variables are non-stationary.

	Model 7	Model 8	Model 9	Model 10	Model 11
lnGDP_it	5.074	5.309	6.126	4.434	4.429
	(0.000***)	(0.000***)	(0.000***)	(0.000***)	(0.000***)
lnGDPit2	-0.275	-0.28	-0.322	-0.232	-0.215
	(0.000***)	(0.000***)	(0.000***)	(0.000***)	(0.003***)
lnENER_it	0.344	0.595	0.488	0.493	0.787
	(0.302)	(0.076*)	(0.139)	(0.129)	(0.042**)
lnRENT_it	-0.035	-0.005	-0.015	-0.03	-0.013
	(0.493)	(0.927)	(0.765)	(0.551)	(0.821)
lnURB_it	-0.652	-0.396	-0.571	-0.572	-1.187
	(0.205)	(0.45)	(0.262)	(0.255)	(0.048*)
lnBFI_it	0.43
	(0.022**)
lnMFI_it		0.824
		(0.009***)
lnTFI_it			-0.497
			(0.003***)
lnIFI_it				-0.187
				(0.01**)
lnFFI_it					0.125
					(0.378)
R^2	0.987	0.987	0.987	0.987	0.986

Note: p-value

***, **, and * show significance at 1%, 5%, and 10% level respectively.

Source: Authors’ estimations.

4. Conclusions and policy implications

In this study, data from 17 countries in Asia-Pacific during the period 2000 to 2017 were utilized to observe the efficiency of determinants of fishing footprints. Numerous indices of economic freedom along with control variables have been modeled in this research. Diagnostic tests ruled out the existence of spatial effects on how variables affect, so that fishing footprint management policy should be policy-based and should be done within countries. Furthermore, results of the investigation confirm the Kuznets curve hypothesis in the fisheries sector of the studied countries. Level of significance and sum of squared error of coefficients of GDP per capita indicates that in the initial stages of economic development, the development of new technologies for collecting aquatic resources and expanding sea food consumption standards leads to more extraction from fishing resources. Such an upward trend in fish extraction is decreasing and the intensity of extraction is gradually decreasing so that it may even decrease from one point.

The intensity of energy use as an indicator of efficiency in production processes does not show significant effects on the footprint of fishing, which means that the challenges in how to extract fishery resources have a higher priority than improving production procedures and efficiency in this field. Rents from natural resources, which in most studies have had a negative effect on ecological footprints, do not show significant effects on fishing footprints. The expansion of natural resource rents, such as oil, leads to the emergence of windfall revenues for countries. They reduce production incentives and economic pressure on other environmental resources and there is a substitute. According to the results, the existence of earned income from other natural resources does not lead to a reduction in environmental pressure in the extraction of fishery resources and alternatives so that in this regard, consumption patterns of fish are located in a higher priority than earned income from the extraction of other resources.

The development of urbanization also improves movement from rural areas to urban ones and reduces the environmental pressure of natural resource consumption in rural areas. The negative effect of urbanization on fishing footprint is significant only in a number of models so that the move towards urbanization has not had significant effects on reducing the extraction of fishing resources.

4.1. Policy implications

Predominantly based on the outcomes, the economic freedom outcomes on fishery footprint are incredible for some of indices which includes government integrity, tax burden, commercial enterprise freedom, monetary freedom. However, such results seem may be traced to their consequent effects on economic explosion which lead to stepped forward great existence and expanded consumption and less complicated to get right of entry to goods and aids in countries. The improved authorities’ integrity by using lowering systemic corruption ends in expanded financial growth due to the fact corruption compromises the transparency that is critical for the green functioning of a free market. Furthermore, the tax burden, on the other hand, lower the man or woman’s reward for their financial activities and decrease the incentive to approve work at all. Higher tax rates, then again, minimize the ability of individuals and firms to pursue their goals inside the marketplace and thereby lower the level of overall non-public-area pastime. Commercial enterprise freedom is another indicator of growing economic boom due to the fact redundant regulations are the maximum commonplace obstacles to the free behavior of entrepreneurial distraction that growth production prices and make it difficult for entrepreneurs to be triumphant within the marketplace. Eventually, economic freedom is some other thing in economic growth and extra strain on the manufacturing and intake of fishing sources. Both inflation and price controls mislead market activity. With a financial coverage that endeavors to fight inflation, keep price balance, and maintain the nation’s wealth, human beings can depend upon marketplace costs for the predictable future. Investments, financial savings, and different long-term plans may be made more confidently. An inflationary policy, with the aid of assessment, confiscates wealth like an invisible tax and distorts costs, mismanages sources, and raises the cost of doing business.

Furthermore, a few indexes of financial freedom, which include change freedom and investment freedom lead to a reduction in environmental pressure and decreased fishery footprint. The reason for such effects may be recognized to the wonderful outcomes of such freedoms at the business community, so that it gives an investment environment in industries primarily based at the export growth or different industries no longer related to the fisheries zone so that a form of alternative is executed inside the production quarter. The qualification to which authorities delays the free draft of foreign trade has an immediate bearing at the ability of people to pursue their financial dreams and maximize their productiveness. In many cases, substitute obstacles also placed superior-era services and products past the reach of neighborhood marketers, restricting their product improvement. Moreover, some unfastened and open investment environments provide maximum business opportunities and incentives for prominent economic interest, extra productivity, and process manufacturing.