دورية أكاديمية
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Coping with the tale of natural resources and environmental inequality: an application of the machine learning tools.

التفاصيل البيبلوغرافية
العنوان: Coping with the tale of natural resources and environmental inequality: an application of the machine learning tools.
المؤلفون: Souissi B; Faculty of Economics and Management, University of Sfax, Sfax, Tunisia., Tiba S; Faculty of Economics and Management, University of Sfax, Sfax, Tunisia. sofienetiba@gmail.com.
المصدر: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 Aug; Vol. 31 (40), pp. 52841-52854. Date of Electronic Publication: 2024 Aug 20.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499 (Electronic) Linking ISSN: 09441344 NLM ISO Abbreviation: Environ Sci Pollut Res Int Subsets: MEDLINE
أسماء مطبوعة: Publication: <2013->: Berlin : Springer
Original Publication: Landsberg, Germany : Ecomed
مواضيع طبية MeSH: Machine Learning* , Natural Resources*, Socioeconomic Factors ; Algorithms ; Environment ; Humans ; Conservation of Natural Resources
مستخلص: With the rising momentum according to the environmentalist voices seeking climate justice for more equity and the importance of encouraging environmental justice mechanisms and tools, in this perspective, the objective of this study is to analyze in depth the substantial role of natural resources abundance in the environmental inequality issue. For this purpose, this study adopted the eXtreme Gradient Boosting (XGBoost), LightGBM, Natural Gradient Boosting (NGBoost), Hybrid hybrid upper confidence bound-long short-term memory-Genetic Algorithm (UCB-LSTM-GA), and the Shapley Additive Explanation (SAE) machine learning algorithms in the context of 21 emerging economies spanning the years 2001 to 2019. The empirical results reveal that natural resource abundance, foreign trade, and foreign direct investment inflows contribute all to higher levels of environmental inequality. However, higher levels of per capita income, gross fixed capital formation, and institutional quality contribute to lower levels of environmental inequality. Addressing climate justice holistically through an integrated supranational vision is significant since every step taken toward eradicating environmental racism matters.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
References: Abu, M., Akurugu, B.A., Egbueri, J.C. (2024). Understanding groundwater mineralization controls and the implications on its quality (Southwestern Ghana): insights from hydrochemistry, multivariate statistics, and multi-linear regression models. Acta Geophys https://doi.org/10.1007/s11600-023-01271-6.
Agbasi JC, Egbueri JC (2023) Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study. J Sediment Environ 8:57–79. (PMID: 10.1007/s43217-023-00124-y)
Agbasi JC, Egbueri JC (2024) Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review. Environ Sci Pollut Res 31:30370–30398. (PMID: 10.1007/s11356-024-33350-6)
Arya K, Raha A, Roy D (2021) Shapley Additive Explanations for explainable artificial intelligence in computer vision. arXiv preprint arXiv:2104.07155.
Atkinson AB (1970) On the measurement of inequality. J Econ Theory 2:244–263. (PMID: 10.1016/0022-0531(70)90039-6)
Baležentis T, Liobikienė G, Štreimikienė D, Sun K (2020) The impact of income inequality on consumption-based greenhouse gas emissions at the global level: a partially linear approach. J Environ Manage 267:110635. (PMID: 10.1016/j.jenvman.2020.110635)
Brulle RJ, Pellow DN (2006) Environmental justice: human health and environmental inequalities. Annu Rev Public Health 27:103–124. (PMID: 10.1146/annurev.publhealth.27.021405.102124)
Chen X, Hu S, Wang H (2021) Shapley Additive Explanations for clinical decision support systems. IEEE J Biomed Health Inform 25(6):1966–1976.
Chen L, Wang Y, Zhang S (2022) Interpreting sentiment analysis models using Shapley Additive Explanation. J Artif Intell Res 45:789–801.
Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, pp 785–794.  https://doi.org/10.1145/2939672.2939785.
Chui KT, Gupta BB, Vasant P (2021) A genetic algorithm optimized RNN-LSTM model for remaining useful life prediction of turbofan engine. Electronics 10(3):285. (PMID: 10.3390/electronics10030285)
Costantini V, Monni S (2008) Environment, human development and economic growth. Ecol Econ 64:867–880. (PMID: 10.1016/j.ecolecon.2007.05.011)
Dalton H (1920) The measurement of the inequality of incomes. Econ J 30(119):348–361. (PMID: 10.2307/2223525)
Dombrowski L, Imhoff M, Beck J (2021) Interpretable machine learning in finance: a shapley value-based framework. J Mach Learn Res 22(90):1–31.
Duan T, Bai S, Zhu J, Zheng A (2020) NGBoost: Natural gradient boosting for probabilistic prediction. In Conference on Neural Information Processing Systems (NeurIPS), pp 16432–16442.
Egbueri JC, Agbasi JC (2022) Performances of MLR, RBF-NN, and MLP-NN in the evaluation and prediction of water resources quality for irrigation purposes under two modeling scenarios. Geocarto Int 37(26):14399–14431. (PMID: 10.1080/10106049.2022.2087758)
Egbueri JC, Unigwe CO, Agbasi JC, Nwazelibe VE (2023) Indexical and artificial neural network modeling of the quality, corrosiveness, and encrustation potential of groundwater in industrialized metropolises, Southeast Nigeria. Environ Dev Sustain 25:14753–14783. (PMID: 10.1007/s10668-022-02687-8)
Fischer T, Lundtofte F (2020) Unequal returns: using the Atkinson index to measure financial risk. J Bank Finance 116:105819. (PMID: 10.1016/j.jbankfin.2020.105819)
Foltz J, Guo Y, Yao Y (2020) Lineage networks, urban migration and income inequality: evidence from rural China. J Comp Econ 48(2):465–482. (PMID: 10.1016/j.jce.2020.03.003)
Gini C (1921) Measurement of inequality of incomes. Econ J 31(121):124–126. (PMID: 10.2307/2223319)
Guo W, Yang B, Ji J, Liu X (2023) Abundance of natural resources, government scale and green economic growth: An empirical study on urban resource curse. Resour Policy 87:104303. (PMID: 10.1016/j.resourpol.2023.104303)
He Q, Fang H, Ji H, Fang S (2017) Environmental inequality in China: a “pyramid model” and nationwide pilot analysis of prefectures with sources of industrial pollution. Sustainability 9:1871. (PMID: 10.3390/su9101871)
Hedenus F, Azar C (2005) Estimates of trends in global income and resource inequalities. Ecol Econ 55(3):351–364. (PMID: 10.1016/j.ecolecon.2004.10.004)
Hossain ME, Islam MS, Bandyopadhyay A, Awan A, Hossain MR, Rej S (2022) Mexico at the crossroads of natural resource dependence and COP26 pledge: Does technological innovation help? Resour Policy 77:102710. (PMID: 10.1016/j.resourpol.2022.102710)
Jorgenson AK, Schor JB, Knight KW, Huang X (2016) September. Domestic inequality and carbon emissions in comparative perspective. Sociol Forum 31:770–786. (PMID: 10.1111/socf.12272)
Kaufman D, Kraay A, Mastruzzi M (2008) Governance matters VII: aggregate and individual governance indicators 1996–2007. World Bank policy research working paper no. 4654. Retrieved from: https://www.govindicators.org/.
Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q (2017) LightGBM: A highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems (NeurIPS), pp 3149–3157.
Knight KW, Schor JB, Jorgenson AK (2017) Wealth inequality and carbon emissions in high-income countries. Social Currents 4(5):403–412. (PMID: 10.1177/2329496517704872)
Koh E, Swaminathan A, Yoon J (2020) A survey on interpretability and explainability in machine learning: taxonomy, empirical analysis, and recent trends. arXiv preprint arXiv:2012.07812.
Kolm SC (1976a) Unequal inequalities. I J Econ Theory 12(3):416–442. (PMID: 10.1016/0022-0531(76)90037-5)
Kolm SC (1976b) Unequal inequalities. II J Econ Theory 13(1):82–111. (PMID: 10.1016/0022-0531(76)90068-5)
Liu Y, Wang M, Feng C (2020) Inequalities of China’s regional low carbon development. J Environ Manage 274:111042. (PMID: 10.1016/j.jenvman.2020.111042)
Liu T, Zhou B, Li S, Gao X, Wang J (2021) User-level explanations for collaborative filtering recommender systems: a Shapley value perspective. IEEE Trans Syst, Man, Cybern: Syst 51(3):1523–1535.
Lozano JA, Klemperer P, Välimäki J (2021) Shapley additive explanations: an overview. Oxford Handbook of Economics of Networks, pp 1–29.
Lundberg SM, Erion GG, Lee S, Wright MN, Raji ID (2020) From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2(1):56–67. (PMID: 10.1038/s42256-019-0138-9)
Ma X, Yang T, Zhang Y, Liu S, Zhang C (2022) A survey on Shapley Additive Explanations in computer vision for explainable AI. Pattern Recogn Lett 154:12–19.
Mi Z, Zheng J, Meng J, Ou J, Hubacek K, Coffman DM, Stern N, Liang S, Wei YM (2020) Economic development and converging household carbon footprints in China. Nat Sustain 3(7):529–537. (PMID: 10.1038/s41893-020-0504-y)
Nwazelibe VE, Egbueri JC, Unigwe CO, Agbasi JC, Ayejoto DA, Abba SI (2023) GIS-based landslide susceptibility mapping of Western Rwanda: an integrated artificial neural network, frequency ratio, and Shannon entropy approach. Environ Earth Sci 82:439. (PMID: 10.1007/s12665-023-11134-4)
Pande CB, Egbueri JC, Costache R, Sidek LM, Wang Q, Alshehri F, Din NM, Gautam VK, Pal SC (2024) Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development. J Clean Prod 444:141035. (PMID: 10.1016/j.jclepro.2024.141035)
Pinto PM, Santos L, Bação F (2022) Explaining social phenomena with Shapley Additive Explanations. Soc Networks 69:26–38.
Salazar DJ, Clauson S, Abel TD, Clauson A (2019) Race, income, and environmental inequality in the US States, 1990–2014. Soc Sci Q 100:592–603. (PMID: 10.1111/ssqu.12608)
Shapley LS (1953) A value for n-person games. Contributions Theory Games 2(28):307–317.
Shorrocks A, Slottje D (2002) Approximating unanimity orderings: an application to Lorenz dominance. J Econ 77(1):91–117. (PMID: 10.1007/BF03052501)
Singh G, Singh J, Wani OA, Egbueri JC, Agbasi JC (2024) Assessment of groundwater suitability for sustainable irrigation: a comprehensive study using indexical, statistical, and machine learning approaches. Groundw Sustain Dev 24:101059. (PMID: 10.1016/j.gsd.2023.101059)
Smith J, Johnson A, Chen L (2023) Interpreting deep neural network predictions using Shapley Additive Explanation. J Mach Learn Res 123:456–789.
Souissi, B., Ghorbel, A. (2023). Machine learning and fuzzy MCDM for digital advertising effectiveness. In: Mirzazadeh, A., Erdebilli, B., Babaee Tirkolaee, E., Weber, GW., Kar, A.K. (eds) Science, Engineering Management and Information Technology. SEMIT 2022. Communications in Computer and Information Science, vol 1808. Springer, Cham. https://doi.org/10.1007/978-3-031-40395-8&#95;24.
Souissi B, Ghorbel A (2022) Upper confidence bound integrated genetic algorithm-optimized long short-term memory network for click-through rate prediction. Appl Stoch Model Bus Ind 38(2):475–496. (PMID: 10.1002/asmb.2671)
Tang J, Zeng J, Wang Y, Yuan H, Liu F, Huang H (2021a) Traffic flow prediction on urban road network based on license plate recognition data: combining attention-LSTM with genetic algorithm. Transportmetrica a: Transport Sci 17(4):1217–1243. (PMID: 10.1080/23249935.2020.1845250)
Tang Z, Han X, Tan M, Tang J, Wang X (2021b) A survey on the interpretability of deep learning in natural language processing. arXiv preprint arXiv:2110.03195.
Statistical decomposition analysis. North-Holland Publishing Co, Amsterdam.
Tiba S (2019) Modeling the nexus between resources abundance and economic growth: an overview from the PSTR model. Resour Policy 64:101503. (PMID: 10.1016/j.resourpol.2019.101503)
Tiba S (2021) The oil abundance and oil dependence scenarios: the bad and the ugly? Environ Model Assess 26(3):283–294. (PMID: 10.1007/s10666-020-09737-3)
Tiba S (2023) Unlocking the poverty and hunger puzzle: toward democratizing the natural resource for accomplishing SDGs 1&2. Resour Policy 82:103516. (PMID: 10.1016/j.resourpol.2023.103516)
Tiba S, Belaid F (2021) Modeling the nexus between sustainable development and renewable energy: the African perspectives. J Econ Surv 35(1):307–329. (PMID: 10.1111/joes.12401)
Wang M, Feng C (2021) The inequality of China’s regional residential CO2 emissions. Sustain Prod Consumption 27:2047–2057. (PMID: 10.1016/j.spc.2021.05.003)
Wang M, Feng C (2022) Tracking the inequalities of global per capita carbon emissions from perspectives of technological and economic gaps. J Environ Manage 315:115144. (PMID: 10.1016/j.jenvman.2022.115144)
Wang C, Guo Y, Shao S, Fan M, Chen S (2020a) Regional carbon imbalance within China: an application of the Kaya-Zenga index. J Environ Manage 262:110378. (PMID: 10.1016/j.jenvman.2020.110378)
Wang Z, Dong R, Zhang L, Li H (2020b) Shapley value-based explanations for collaborative filtering recommender systems. IEEE Transactions Neural Networks Learning Syst 32(4):1374–1385.
Wilkinson RG, Pickett KE (2009) Income inequality and social dysfunction. Ann Rev Sociol 35:493–511. (PMID: 10.1146/annurev-soc-070308-115926)
World Development Indicator Database (CD ROM-2023). Retrieved from: https://datatopics.worldbank.org/world-development-indicators/ . Accessed 17 Dec 2023.
Wu D, Yuan L, Li R, Li J (2018) Decomposing inequality in research funding by university-institute sub-group: a three-stage nested Theil index. J Informet 12(4):1312–1326. (PMID: 10.1016/j.joi.2018.10.007)
Xu X, Han L, Lv X (2016) Household carbon inequality in urban China, its sources and determinants. Ecol Econ 128:77–86. (PMID: 10.1016/j.ecolecon.2016.04.015)
Yang X, Feng K, Su B, Zhang W, Huang S (2019) Environmental efficiency and equality embodied in China’s inter-regional trade. Sci Total Environ 672:150–161. (PMID: 10.1016/j.scitotenv.2019.03.450)
Zhang W, Liu Y, Feng K, Hubacek K, Wang J, Liu M, Jiang L, Jiang H, Liu N, Zhang P (2018) Revealing environmental inequality hidden in China’s interregional trade. Environ Sci Technol 52:7171–7181. (PMID: 10.1021/acs.est.8b00009)
Zhang Q, Wang R, Tang D, Boamah V (2023) The role and transmission mechanism of forest resource abundance on low-carbon economic development in the Yangtze River Delta region: insights from the COP26 targets. Resour Policy 85:103944. (PMID: 10.1016/j.resourpol.2023.103944)
Zheng S, Yao R, Zou K (2022) Provincial environmental inequality in China: measurement, influence, and policy instrument choice. Ecol Econ 200:107537. (PMID: 10.1016/j.ecolecon.2022.107537)
فهرسة مساهمة: Keywords: Climate justice; Environmental inequality; Environmental racism; Machine learning; Natural resource abundance
تواريخ الأحداث: Date Created: 20240820 Date Completed: 20240906 Latest Revision: 20240925
رمز التحديث: 20240926
DOI: 10.1007/s11356-024-34737-1
PMID: 39162896
قاعدة البيانات: MEDLINE
الوصف
تدمد:1614-7499
DOI:10.1007/s11356-024-34737-1