How Can the Blue Economy Contribute to Inclusive Growth and Ecosystem Resources in Asia? A Comparative Analysis
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Justification of Independent Variables
3.2. Econometric Methods
3.2.1. Panel Heteroscedasticity Test
3.2.2. Panel Autocorrelation Test
3.2.3. Panel Unit Root (CIPS) Test
3.2.4. Panel Cointegration Test of Westerlund
3.2.5. Long-Run Estimation Method
4. Results
4.1. Descriptive Analysis
4.2. Preliminary Tests
4.3. Regression Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
(IG) | Inclusive Growthgrowth |
(FP) | Fishery Productionproduction |
(AP) | Aquaculture Productionproduction |
(AFF) | Agriculture, Forestryforestry, and Fishingfishing |
(CL) | Capital Labour–labor |
(TO) | Trade Opennessopenness |
(EF) | Ecological Footprintfootprint |
(REU) | Renewable Energy Utilizationenergy utilization |
References
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Author(s) | Variables | Methodology | Findings |
---|---|---|---|
[45] | 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. |
[46] | 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. |
[47] | 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. |
[48] | 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 for interdisciplinary strategies, such as encouraging sustainable economic growth, guaranteeing fair political involvement, and supporting social safety nets and capacity-building initiatives to promote inclusive growth and development. |
[49] | 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. |
[50] | 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. |
[51] | Capital–labor | 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. |
[52] | 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. |
[53] | 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. |
[54] | 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. |
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 | |
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 |
Problem | Test | Lower Middle | Upper Middle | High Income | |||
---|---|---|---|---|---|---|---|
Test.stat | Prob. | Test.stat. | Prob. | Test.stat. | Prob. | ||
Cross S.D | Breusch and 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 | Modified Wald Breusch–Pagan/Cook–Weisberg Wooldridge | 21,889.68 * 14.06 *** | 0.000 0.000 | 11,466.28 * 7.55 *** | 0.000 0.000 | 32,884.53 * 44.82 *** | 0.000 0.000 |
Autocorrelation | 228.93 *** | 0.000 | 12.786 *** | 0.000 | 208.76 *** | 0.000 |
Variables | Lower Middle | Upper Middle | High Income |
---|---|---|---|
At level (intercept and 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 |
LNEF | −3.063 *** | −2.552 | −3.457 *** |
LNRE | −2.156 *** | 2.899 | 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 *** |
LNEF | −3.22 *** | 3.456 *** | 3.111 *** |
LNRE | −4.165 *** | −3.707 *** | −3.251 *** |
Panel | Variance Ratio | |
---|---|---|
Statis. | Prob. | |
LMYCs | 3.485 ** | 0.0005 |
UMYCs | 4.283 *** | 0.0000 |
HYCs | 4.223 *** | 0.0000 |
Variable | Lower Middle | Upper Middle | High Income | ||||||
---|---|---|---|---|---|---|---|---|---|
Coff. | Std. Er. | Prob. | Coff. | Std. Er. | Prob. | Coff. | Std. Er. | Prob. | |
lnIG | −0.535 *** | 0.055 | 0.000 | 0.682 *** | 0.053 | 0.000 | 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 | 0.000 | 0.053 ** | 0.033 | 0.055 | 0.222 *** | 0.041 | 0.008 |
lnTOP | 0.345 *** | 0.090 | 0.006 | 0.544 *** | 0.248 | 0.000 | 2.296 *** | 0.083 | 0.002 |
lnAFP | 0.788 ** | 0.004 | 0.080 | −0.889 ** | 0.009 | 0.017 | 0.023 *** | 0.011 | 0.007 |
LnRE | −0.345 *** | 0.005 | 0.030 | −0.576 *** | 0.003 | 0.012 | 0.896 *** | 0.010 | 0.005 |
F-Stat | 177.44 *** (0.000) | 134.29 *** (0.000) | 81.89 *** (0.000) | ||||||
R2 | 0.566 | 0.678 | 0.360 |
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Geng, B.; Wu, D.; Zhang, C.; Xie, W.; Mahmood, M.A.; Ali, Q. How Can the Blue Economy Contribute to Inclusive Growth and Ecosystem Resources in Asia? A Comparative Analysis. Sustainability 2024, 16, 429. https://doi.org/10.3390/su16010429
Geng B, Wu D, Zhang C, Xie W, Mahmood MA, Ali Q. How Can the Blue Economy Contribute to Inclusive Growth and Ecosystem Resources in Asia? A Comparative Analysis. Sustainability. 2024; 16(1):429. https://doi.org/10.3390/su16010429
Chicago/Turabian StyleGeng, Biao, Daoning Wu, Chengshu Zhang, Wenbao Xie, Muhammad Aamir Mahmood, and Qamar Ali. 2024. "How Can the Blue Economy Contribute to Inclusive Growth and Ecosystem Resources in Asia? A Comparative Analysis" Sustainability 16, no. 1: 429. https://doi.org/10.3390/su16010429