The transition to a circular carbon economy (CCE) is critical for reducing CO2 emissions and climate change mitigation. However, existing studies have overlooked the role of ecological foundations such as biocapacity and renewable energy. As a results, most of these struggle to establish the extent of the complexities underpining the interaction amongst the drivers of carbon economies. This study addresses these gaps by employing advanced machine learning (ML) models (Random Forest and Gradient Boosting) and linear fixed effects model to analyze data from 16 belt and road initiative (BRI) countries from 2000 to 2022. The results predicted and quantified the impact and the relative importance of renewable energy, technological innovation, and biocapacity on CO2 emissions across 16 Upper-Middle Income countries in the Belt and Road Intiative (BRI) region. Both the RF and GB models demonstrated high predictive accuracy and robustness (R2 > 0.98 on the full dataset). The linear FE model also achieved an R2 (within) of 0.74, confirming the presence of significant unobserved country-level heterogeneity across the region. The most significant finding reveals that biocapacity surpasses both renewable energy and technological innovation as the most influential predictor of CO2 emissions across the BRI region. This underscores the critical, yet neglected, role of ecological carrying capacity as the foundation for a circular carbon loop. This study contributes a novel, data-driven methodology for sustainability transition analysis and offers a crucial policy insight. The study therefore noted that achieving carbon circularity in the BRI region requires a re-prioritization towards biocapacity protection as a non-negotiable prerequisite, alongside the acceleration of clean energy and technology adoption.
GawusuSAhmedA. Africa’s transition to cleaner energy: regulatory imperatives and governance dynamics. In: Energy regulation in Africa: dynamics, challenges, and opportunities. Switzerland: Springer, 2024, pp.25–51.
2.
HwangYK and Sánchez DíezÁ. Renewable energy transition and green growth nexus in Latin America. Renewable Sustainable Energy Rev, 2024; 98.
3.
RidwanILSenathirajahARBSAl-FaryanMAS. Investigating the asymmetric effects of financial development on trade performance in Africa: can digitalization, transport services, and regulatory quality drive the vision 2063?J Econ Asymmetr2024; 30: e00390.
4.
TiwariSMohammedKSMentelG, et al.Role of circular economy, energy transition, environmental policy stringency, and supply chain pressure on CO2 emissions in emerging economies. Geosci Front2024; 15: 101682.
5.
AlsarhanLMAlayyarASAlqahtaniNB, et al.Circular carbon economy (CCE): a way to invest CO2 and protect the environment, a review. Sustainability2021; 13: 11625.
6.
MeysRKätelhönABachmannM, et al.Achieving net-zero greenhouse gas emission plastics by a circular carbon economy. Science2021; 374: 71–76.
7.
KapitonovIA. Development of low-carbon economy as the base of sustainable improvement of energy security. Environ Dev Sustain2021; 23: 3077–3096.
8.
TcvetkovP. Engagement of resource-based economies in the fight against rising carbon emissions. Energy Rep2022; 8: 874–883.
9.
D’amatoDKorhonenJ. Integrating the green economy, circular economy and bioeconomy in a strategic sustainability framework. Ecol Econ2021; 188: 107143.
10.
YangFIbrahimRLAjideKB, et al.Examining the ecological effects of energy transition, environmental technology, and structural change in BRICS economies: implications for sustainable development. Energy Sour Part B: Econ, Plan, Policy2024; 19: 2419956.
11.
ZhangXYuGIbrahimRL, et al.Greening the E7 environment: how can renewable and nuclear energy moderate financial development, natural resources, and digitalization towards the target?Int J Sustain Dev World Ecol2024; 31: 447–465.
12.
AliNPhoungthongKTechatoK, et al.FDI, green innovation and environmental quality nexus: new insights from BRICS economies. Sustainability2022; 14: 2181.
13.
PapadisETsatsaronisG. Challenges in the decarbonization of the energy sector. Energy2020; 205. 10.1016/j.energy.2020.118025
14.
UdeaghaMCNgepahN. The drivers of environmental sustainability in BRICS economies: Do green finance and fintech matter?World Dev Sustain2023; 3. 10.1016/j.wds.2023.100096
15.
NiccolucciVTiezziEPulselliF, et al.Biocapacity vs ecological footprint of world regions: a geopolitical interpretation. Ecol Indic2012; 16: 23–30.
ToderoiuF. Ecological footprint and biocapacity–methodology and regional and national dimensions. Agric Econ Rur Dev2010; 2: 213–238.
18.
Moros-OchoaMACastro-NietoGYQuintero-EspañolA, et al.Forecasting biocapacity and ecological footprint at a worldwide level to 2030 using neural networks. Sustainability2022; 14: 10691.
19.
EcheverríaDRothJMostafaM, et al. Circular economy proxy measures: Indicators on jobeffects for a closed-loop economy. International Institute for Sustainable Development, 2020, https://www.iisd.org/system/files/publications/circular-economy-proxy-measures.pdf.
20.
KirikkaleliDSofuoğluEOjekemiO. Does patents on environmental technologies matter for the ecological footprint in the USA? Evidence from the novel Fourier ARDL approach. Geosci Front2023; 14. 10.1016/j.gsf.2023.101564
21.
KirchherrJYangN-HNSchulze-SpüntrupF, et al.Conceptualizing the circular economy (revisited): an analysis of 221 definitions. Resour Conserv Recycl2023; 194: 107001.
22.
XuDAbbasSRafiqueK, et al.The race to net-zero emissions: can green technological innovation and environmental regulation be the potential pathway to net-zero emissions?Technol Soc2023; 75: 102364.
23.
AliNPhoungthongKKhanA, et al.Does FDI foster technological innovations? Empirical evidence from BRICS economies. Plos one2023; 18: e0282498.
24.
RaufAAliNSadiqMN, et al.Foreign direct investment, technological innovations, energy use, economic growth, and environmental sustainability nexus: new perspectives in BRICS economies. Sustainability2023; 15: 14013–14032.
25.
LuomiMYilmazFAl ShehriT, et al. The circular carbon economy index–methodologicalapproach and conceptual frameworks (No. ks--2021-mp01). King Abdullah Petroleum Studies and Research Center, 2021.
26.
HussainJZhouKMuhammadF, et al.Renewable energy investment and governance in countries along the Belt & Road Initiative: does trade openness matter?Renew Energy2021; 180: 1278–1289.
NedopilC. China Belt and Road Initiative (BRI) investment report 2023. Shanghai, China: Griffith Asia Institute, 2024.
30.
LiXDuJLongH. Theoretical framework and formation mechanism of the green development system model in China. Environ Dev2019; 32: 100465.
31.
BressanelliGAdrodegariFPigossoDC, et al.Towards the smart circular economy paradigm: a definition, conceptualization, and research agenda. Sustainability2022; 14: 4960.
32.
AddaiKAl GeitanySHAthariSA, et al.Do environmental tax and energy matter for environmental degradation in the UK? Evidence from novel Fourier-based estimators. Energies2024; 17: 5732.
33.
KahiaMJarrayaBkahouliB, et al.The role of environmental innovation and green energy deployment in environmental protection: evidence from Saudi Arabia. J Knowl Econ2022; 15: 337–363.
34.
AbbasSGuiPChenA, et al.The effect of renewable energy development, market regulation, and environmental innovation on CO2 emissions in BRICS countries. Environ Sci Pollut Res2022; 29: 59483–59501.
35.
AthariSA. Global economic policy uncertainty and renewable energy demand: does environmental policy stringency matter? Evidence from OECD economies. J Cleaner Prod2024a; 450: 141865.
36.
AthariSA. The impact of financial development and technological innovations on renewable energy consumption: do the roles of economic openness and financial stability matter in BRICS economies?Geol J2024b; 59: 288–300.
37.
WangQZhangSLiR. Artificial intelligence in the renewable energy transition: the critical role of financial development. Renew Sustain Energy Rev2026; 226: 116280.
38.
RadmehrRHenneberrySRShayanmehrS. Renewable energy consumption, CO2 emissions, and economic growth nexus: a simultaneity spatial modeling analysis of EU countries. Struct Change Econ Dyn2021; 57: 13–27.
39.
SaidiKOmriA. The impact of renewable energy on carbon emissions and economic growth in 15 major renewable energy-consuming countries. Environ Res2020; 186: 109567.
40.
SalemHSPudzaMYYihdegoY. Harnessing the energy transition from total dependence on fossil to renewable energy in the Arabian Gulf region, considering population, climate change impacts, ecological and carbon footprints, and United Nations’ Sustainable Development Goals. Sustain Earth Rev2023; 6: 10.
41.
StramBN. Key challenges to expanding renewable energy. Energy Policy2016; 96: 728–734.
42.
SaadWTalebA. The causal relationship between renewable energy consumption and economic growth: evidence from Europe. Clean Technol Environ Policy2018; 20: 127–136.
43.
GasparatosADollCNEstebanM, et al.Renewable energy and biodiversity: implications for transitioning to a green economy. Renewable Sustainable Energy Rev2017; 70: 161–184.
44.
AthariSAAddiaKKirikkaleliD, et al.Do environmental taxes and green electricity matter for environmental quality? Fresh evidence in France based on Fourier methods. Energies2025; 18: 5046.
45.
HardiIRaySAttariMUQ, et al.Innovation and economic growth in the top five Southeast Asian economies: a decomposition analysis. Ekonomikalia J Econ2024; 2: 1–14.
46.
ZhaDChenQWangL. Exploring carbon rebound effects in Chinese households’ consumption: a simulation analysis based on a multi-regional input–output framework. Appl Energy2022; 313. 10.1016/j.apenergy.2022.118847
47.
DuKLiPYanZ. Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data. Technol Forecast Soc Change2019; 146: 297–303.
48.
WangHHuYLiangY. Simulation and spatiotemporal evolution analysis of biocapacity in Xilingol based on CA-Markov land simulation. Environ Sustain Indicators2021; 11: 100136.
49.
WackernagelMSchulzNBDeumlingD, et al.Tracking the ecological overshoot of the human economy. Proc Natl Acad Sci USA2002; 99: 9266–9271.
50.
QamruzzamanM. Do natural resources bestow or curse the environmental sustainability in Cambodia? Nexus between clean energy, urbanization, and financial deepening, natural resources, and environmental sustainability. Energy Strategy Rev2024; 53. 10.1016/j.esr.2024.101412
51.
WangYShinwariRHaseeb PayabA, et al.Harmonizing sustainability: unveiling the nexus of public private investment, natural resources, and environmental dynamics by applying ARDL and machine learning approach. Ecol Indic2024; 161. 10.1016/j.ecolind.2024.111931
52.
ChenXLakkanawanitPSuttipunM, et al.Green technology innovation and corporate reputation: key drivers of ESG and firm performance. Emerg Sci J2024; 8: 2501–2518.
53.
ChenYZhangY. The impact of digital transformation on firm’s financial performance: evidence from China. Ind Manag Data Syst2024; 124: 2021–2041.
54.
MurrayASkeneKHaynesK. The circular economy: an interdisciplinary exploration of the concept and application in a global context. J Bus Ethics2017; 140: 369–380.
55.
OgunmakindeOEEgbelakinTSherW. Contributions of the circular economy to the UN sustainable development goals through sustainable construction. Resour Conserv Recycl2022; 178: 106023.
56.
ZhongSZhangKBagheriM, et al. Machine learning: new ideas and tools in environmental science and engineering. Environmental Science & Technology2021; 55: 12741–12754.
57.
ZhuJJJiangJYangM, et al. ChatGPT and environmental research. Environmental Science and Technology2023; 57: 17667–17670.
58.
CastelliMClementeFMPopovičA, et al. A machine learning approach to predict air quality in California. Complexity2020; 2020: 8049504.
59.
LiuTTanZXuC, et al. Study on deep reinforcement learning techniques for building energy consumption forecasting. Energy and Buildings2020; 208: 109675.
60.
Olu-AjayiRAlakaHSulaimonI, et al. Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. Journal of Building Engineering2022; 45: 103406.
61.
PhamA-DNgoN-TTruongTTH, et al. Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability. Journal of Cleaner Production2020; 260: 121082.
62.
TahmasebiPKamravaSBaiT, et al. Machine learning in geo-and environmental sciences: from small to large scale. Advances in Water Resources2020; 142: 103619.
63.
XiangXLiQKhanS, et al. Urban water resource management for sustainable environment planning using artificial intelligence techniques. Environmental Impact Assessment Review2021; 86: 106515.
64.
AdebayoTSIbrahimRLAgyekumEB, et al.The environmental aspects of renewable energy consumption and structural change in Sweden: a new perspective from wavelet-based granger causality approach. Heliyon2022; 8: e10697.
65.
AliSYusopZKaliappanSR, et al.Dynamic common correlated effects of trade openness, FDI, and institutional performance on environmental quality: evidence from OIC countries. Environ Sci Pollut Res2020; 27: 11671–11682.
66.
AlmasriRANarayanS. A recent review of energy efficiency and renewable energy in the Gulf Cooperation Council (GCC) region. Int J Green Energy2021; 18: 1441–1468.
67.
AlmulhimAIAl-SaidiM. Circular economy and the resource nexus: realignment and progress towards sustainable development in Saudi Arabia. Environ Dev2023; 46: 100851.
68.
TangFIshwaranH. Random forest missing data algorithms. Statistical analysis and data mining. The ASA Data Science Journal2017; 10: 363–377.
69.
GoldsteinBAHubbardAECutlerA, et al.An application of random forests to a genome-wide association dataset: methodological considerations & new findings. BMC Genet2010; 11: 1–13.
70.
WuJLiC. Illustrating the nonlinear effects of urban form factors on transportation carbon emissions based on gradient boosting decision trees. Sci Total Environ2024; 929: 172547.
71.
AhmadMWReynoldsJRezguiY. Predictive modelling for solar thermal energy systems: a comparison of support vector regression, random forest, extra trees and regression trees. J Cleaner Prod2018; 203: 810–821.
72.
KiangalaSKWangZ. An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an Industry 4.0 environment. Mach Learn Appl2021; 4: 100024.
73.
AbedMImteazMAAhmedAN, et al.Modelling monthly pan evaporation utilising random forest and deep learning algorithms. Sci Rep2022; 12: 13132.
74.
GanVJLoIMMaJ, et al.Simulation optimisation towards energy efficient green buildings: current status and future trends. J Cleaner Prod2020; 254: 120012.
75.
BelaïdFRanjbarZMassiéC. Exploring the cost-effectiveness of energy efficiency implementation measures in the residential sector. Energy Policy2021; 150: 112122.
76.
FeketeHKuramochiTRoelfsemaM, et al.A review of successful climate change mitigation policies in major emitting economies and the potential of global replication. Renew Sustain Energy Rev2021; 137: 110602.
