Determinants of Building-Sector CO₂ Emissions in the EU: A Combined Econometric and Machine Learning Approach

Mele, Marco and Costantiello, Alberto and Anobile, Fabio and Leogrande, Angelo (2025): Determinants of Building-Sector CO₂ Emissions in the EU: A Combined Econometric and Machine Learning Approach. Published in:

Abstract

This paper evaluates the structural, environmental, and climatic factors influencing carbon dioxide emissions from the building sector (CBE) in 27 European Union member states from 2005 to 2023. This analysis uses panel data from the World Bank and four econometric models—Random Effects, Fixed Effects, Dynamic Panel GMM, and Weighted Least Squares—coupled with machine learning and clustering to provide a robust analysis of emissions. The econometric models show that all models support a negative relationship between agriculture, forestry, and fishing value added (AFFV) and forest area (FRST), suggesting that a robust rural economy and substantial natural carbon sinks are accompanied by lower emissions in the building sector. On the other hand, water stress (WSTR), PM2.5 pollution, heating and cooling degree days, and nitrous oxide emissions (N2OP) are found to significantly, yet positively, affect CBE. Tests of diagnostic analyses support Fixed Effects and Weighted Least Squares models, whereas results from GMM models are limited by instrument validity violations. In machine learning analysis, K-Nearest Neighbors (KNN) models are found to be most diagnostic, with all performance metrics being improved, establishing a prominent role for coal electricity, water stress, agricultural intensities, and climatic factors. Subsequently, a solution with 10 clusters, selected using Bayesian Information Criteria and silhouettes, identified a set of environmental and economic characteristics based on differences between low- and high-emission groups. High-emitting groups result from agricultural intensification, pollution, and low energy efficiency, while low-emitting groups are associated with renewable energy, low pollution, and a favorable climate. This analysis, hence, presents a multifaceted assessment of building sector emissions, with climatic, structural, and energy transition patterns as driving factors for meeting decarbonization targets for the European Union.

• Item Type: MPRA Paper
• Language: English
• Keywords: Building-sector carbon emissions; Panel data econometrics; Machine learning prediction; Environmental and climatic drivers; Cluster analysis
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C33 - Panel Data Models ; Spatio-temporal Models; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q41 - Demand and Supply ; Prices; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q54 - Climate ; Natural Disasters and Their Management ; Global Warming; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q56 - Environment and Development ; Environment and Trade ; Sustainability ; Environmental Accounts and Accounting ; Environmental Equity ; Population Growth

References

Abualhaj, M. M., Abu-Shareha, A. A., Shambour, Q. Y., Alsaaidah, A., Al-Khatib, S. N., & Anbar, M. (2024). Customized K-nearest neighbors’ algorithm for malware detection. International Journal of Data & Network Science, 8(1).

Akakpo, S., Dambra, P., Paz, R., Smyth, T., Torre, F., & Yu, C. (2024). Optimization of the K-Nearest Neighbor Algorithm to Predict Bank Churn. Statistics, Optimization & Information Computing, 12(5), 1397-1408.

Almusaed, A., Yitmen, I., Myhren, J. A., & Almssad, A. (2024). Assessing the impact of recycled building materials on environmental sustainability and energy efficiency: a comprehensive framework for reducing greenhouse gas emissions. Buildings, 14(6), 1566.

Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Perez, J. M., & nigo Perona, I. (2014). An extensive comparative study of cluster validity indices. Pattern Recognition.

Asdrubali, F., Berardi, U., & Stasi, R. (2025). The impact of the building sector. In Sustainability Certifications, Labels and Tools in the Built Environment (pp. 4-21). Routledge.

Aste, N., Del Pero, C., & Leonforte, F. (2022). Toward building sector energy transition. In Handbook of Energy Transitions (pp. 127-150). CRC Press.

Attia, S., Santos, M. C., Al-Obaidy, M., & Baskar, M. (2021, October). Leadership of EU member States in building carbon footprint regulations and their role in promoting circular building design. In IOP Conference Series: Earth and Environmental Science (Vol. 855, No. 1, p. 012023). IOP Publishing.

Babbar, H., Rani, S., Sah, D. K., AlQahtani, S. A., & Kashif Bashir, A. (2023). Detection of android malware in the Internet of Things through the K-nearest neighbor algorithm. Sensors, 23(16), 7256.

Balaras, C. A., Dascalaki, E. G., Patsioti, M., Droutsa, K. G., Kontoyiannidis, S., & Cholewa, T. (2023). Carbon and greenhouse gas emissions from electricity consumption in European Union Buildings. Buildings, 14(1), 71.

Bărbulescu, A. (2024). Statistical Analysis and Modeling of the CO2 Series Emitted by Thirty European Countries. Climate, 12(3), 34.

Bardulis, A., Purvina, D., Bardule, A., & Lazdins, A. (2023). Potential role of tree introduction in agricultural land to reduce greenhouse gas emissions. In 22nd International Scientific Conference" Engineering for Rural Development": proceedings:[Jelgava, Latvia,] May 24-26, 2023 (Vol. 22, pp. 196-203).

Bezić, H., Mance, D., & Balaž, D. (2022). Panel evidence from EU countries on CO2 emission indicators during the Fourth Industrial Revolution. Sustainability, 14(19), 12554.

Biancalani, F., Gnecco, G., Metulini, R., & Riccaboni, M. (2024). The impact of the European Union emissions trading system on carbon dioxide emissions: a matrix completion analysis. Scientific reports, 14(1), 19676.

Boca, M. C., Bungau, C. C., & Hanga-Farcas, I. F. (2025). Climate-Conscious Sustainable Practices in the Romanian Building Sector. Buildings, 15(12), 2106.

Bonar, D. J. (2024). Do strict environmental policies in European countries reduce CO2 emissions?. Przegląd Statystyczny. Statistical Review, 71(1), 1-22.

Braungardt, S., Bei der Wieden, M., & Kranzl, L. (2025). EU emissions trading in the buildings sector–an ex-ante assessment. Climate Policy, 25(2), 208-222.

Cámara-Aceituno, J., Hermoso-Orzáez, M. J., Terrados-Cepeda, J., Rivadeneira-Zambrano, A., Mena-Nieto, Á., Golpe, A. A., & Garcia-Ramos, J. E. (2025). Exploring the driving forces of CO 2 emissions in the European Union. Open Research Europe, 5, 132.

Cazcarro, I., García-Gusano, D., Iribarren, D., Linares, P., Romero, J. C., Arocena, P., ... & Cadarso, M. Á. (2022). Energy-socio-economic-environmental modelling for the EU energy and post-COVID-19 transitions. Science of the Total Environment, 805, 150329.

Charlier, D., Fodha, M., & Kirat, D. (2023). Residential CO2 emissions in Europe and carbon taxation: a country-level assessment. The Energy Journal, 44(5), 187-206.

Chen, L., Zhang, Y., Chen, Z., Dong, Y., Jiang, Y., Hua, J., ... & Yap, P. S. (2024). Biomaterials technology and policies in the building sector: a review. Environmental Chemistry Letters, 22(2), 715-750.

Choi, Y., An, N., Hong, S., Cho, H., Lim, J., Han, I. S., ... & Kim, J. (2022). Time-series clustering approach for training data selection of a data-driven predictive model: Application to an industrial bio 2, 3-butanediol distillation process. Computers & Chemical Engineering, 161, 107758.

Chovancová, J., Petruška, I., & Litavcová, E. (2021). Dependence of CO2 emissions on energy consumption and economic growth in the European Union: A panel threshold model. Economics and Environment, 78(3), 73-89.

Cialani, C., & Mortazavi, R. (2021). Sectoral analysis of club convergence in EU countries’ CO2 emissions. Energy, 235, 121332.

Cubuk, G. (2023). Comparison of emissions gap reports by building sector between 2014–2022 through data analysis. Journal of Environmental Protection and Ecology, 24(2), 397-407.

Dai, Q., Liu, X. Y., Sun, F. Y., & Ren, F. R. (2024). Ensemble intelligence prediction algorithms and land use scenarios to measure carbon emissions of the Yangtze River Delta: A machine learning model based on Long Short-Term Memory. PloS One, 19(12), e0311441.

Dinca, G., Barbuța, M., Negri, C., Dinca, D., & Model, L. S. (2022). The impact of governance quality and educational level on environmental performance. Front. Environ. Sci, 10, 1-15.

Dominguez, C., Kakkos, E., Gross, D., Hischier, R., & Orehounig, K. (2024). Renovated or replaced? Finding the optimal solution for an existing building considering cumulative CO2 emissions, energy consumption and costs–A case study. Energy and Buildings, 303, 113767.

Doran, N. M., Bădîrcea, R. M., Jianu, E., Antoniu, M. E., Ciobanu, R. M., & Ciobanu, Ș. C. F. (2025). Unveiling CO2 Emission Dynamics Under Innovation Drivers in the European Union. Sustainability, 17(8), 3463.

Doryń, W., & Wawrzyniak, D. (2024). Heterogeneity in air pollution levels and their techno‑economic determinants: a cluster analysis of the EU–27.

Dragonetti, L., Papadaki, D., Mazzoli, C., Monacelli, A., Assimakopoulos, M. N., & Ferrante, A. (2025). Circular deep renovation versus demolition with reconstruction: Environmental and financial evaluation to support decision making in the construction sector. Energy and Buildings, 336, 115610.

Elizabeth Yancey, R., Xin, B., & Matloff, N. (2021). Modernizing k‐nearest neighbors. Stat, 10(1), e335.

Erdogan, S. (2021). Dynamic nexus between technological innovation and building sector carbon emissions in the BRICS countries. Journal of Environmental Management, 293, 112780.

Famiglietti, J., Madioum, H., & Motta, M. (2023). Developing a New Data-Driven LCA Tool at the Urban Scale: The Case of the Embodied Environmental Profile of the Building Sector. Sustainability, 15(15), 11518.

Fotiou, T., Fragkos, P., & Zisarou, E. (2024). Decarbonising the EU Buildings| Model-Based Insights from European Countries. Climate, 12(6), 85.

Gan, L., Liu, Y., & Cai, W. (2023). Carbon neutral projections of public buildings in China under the shared socioeconomic pathways: A tertiary industry perspective. Environmental Impact Assessment Review, 103, 107246.

Gholipour, H. F., Arjomandi, A., & Yam, S. (2022). Green property finance and CO2 emissions in the building industry. Global Finance Journal, 51, 100696.

Giannelos, S., Bellizio, F., Strbac, G., & Zhang, T. (2024). Machine learning approaches for predictions of CO2 emissions in the building sector. Electric Power Systems Research, 235, 110735.

Giannelos, S., Moreira, A., Papadaskalopoulos, D., Borozan, S., Pudjianto, D., Konstantelos, I., ... & Strbac, G. (2023). A machine learning approach for generating and evaluating forecasts on the environmental impact of the buildings sector. Energies, 16(6), 2915.

Guo, Z., & Luo, Y. (2024). A Building Carbon Emission Prediction Model Based on Optimized Machine Learning Model. Innovative Applications of AI, 1(4), 70-83.

Hasler, C., & Tillé, Y. (2016). Balanced k-nearest neighbour imputation. Statistics, 50(6), 1310-1331.

Hassan, O. A., & Rezaei, H. (2025). The Load-Bearing System from the Perspective of Sustainable Building. Green and Low-Carbon Economy, 3(3).

Heinz, E., Sovacool, B. K., Kwan, T., & Petit, V. (2025). The Barriers and Drivers to Decarbonization of the Building Sector in the United States: Qualitative Insights from Boston and Phoenix. Building and Environment, 114032.

Hemmati, M., Messadi, T., Gu, H., Seddelmeyer, J., & Hemmati, M. (2024). Comparison of embodied carbon footprint of a mass timber building structure with a steel equivalent. Buildings, 14(5), 1276.

Hsu, A., Wang, X., Tan, J., Toh, W., & Goyal, N. (2022). Predicting European cities’ climate mitigation performance using machine learning. Nature Communications, 13(1), 7487.

Hu, S., Jiang, Y., Yang, X., Pan, Y., Rong, X., Hao, B., ... & Yan, D. (2025). Ecological Pathway to Achieve Carbon Neutrality in China’s Building Sector. Engineering.

Ikotun, A. M., Habyarimana, F., & Ezugwu, A. E. (2025). Cluster validity indices for automatic clustering: A comprehensive review. Heliyon, 11(2).

Junda, E., & Málaga-Chuquitaype, C. (2025). Life cycle impacts of structural deterioration and seismic events on cross-laminated timber buildings. Journal of Building Engineering, 104, 112282.

Kadrić, D., Aganovic, A., Martinović, S., Delalić, N., & Delalić-Gurda, B. (2022). Cost-related analysis of implementing energy-efficient retrofit measures in the residential building sector of a middle-income country–A case study of Bosnia and Herzegovina. Energy and Buildings, 257, 111765.

Kartal, M. T., Kirikkaleli, D., & Pata, U. K. (2024). Role of environmental policy stringency on sectoral CO2 emissions in EU-5 countries: disaggregated level evidence by novel quantile-based approaches. Energy & Environment, 0958305X241241026.

Kayakuş, M., Terzioğlu, M., Erdoğan, D., Zetter, S. A., Kabas, O., & Moiceanu, G. (2023). European Union 2030 carbon emission target: The case of Turkey. Sustainability, 15(17), 13025.

Koengkan, M., Fuinhas, J. A., Teixeira, M., Kazemzadeh, E., Auza, A., Dehdar, F., & Osmani, F. (2022). The capacity of battery-electric and plug-in hybrid electric vehicles to mitigate CO2 emissions: macroeconomic evidence from european union countries. World Electric Vehicle Journal, 13(4), 58.

Kosowski, P. (2024). From Fossil Fuels to Renewables: Clustering European Primary Energy Production from 1990 to 2022. Energies, 17(22), 5596.

Lenssen, L., & Schubert, E. (2024). Medoid Silhouette clustering with automatic cluster number selection. Information Systems, 120, 102290.

Levada, A. L. M., Nielsen, F., & Haddad, M. F. C. (2024). Adaptive $ k $-nearest neighbor classifier based on the local estimation of the shape operator. arXiv preprint arXiv:2409.05084.

Li, S., Siu, Y. W., & Zhao, G. (2021). Driving factors of CO2 emissions: further study based on machine learning. Frontiers in Environmental Science, 9, 721517.

Li, X., Li, Y., Zhou, H., Fu, Z., Cheng, X., & Zhang, W. (2023). Research on the carbon emission baselines for different types of public buildings in a northern cold areas city of China. Buildings, 13(5), 1108.

Li, Y., Chen, H., Yu, P., & Yang, L. (2024). The application and evaluation of the LMDI method in building carbon emissions analysis: A comprehensive review. Buildings, 14(9), 2820.

Liu, Y., Wu, Y., & Zhu, X. (2024). Industrial clusters and carbon emission reduction: evidence from China. The Annals of Regional Science, 73(2), 557-597.

Magaletti, N., Notarnicola, V., Di Molfetta, M., & Leogrande, A. (2025). Decarbonizing the building sector: the integrated role of environmental, social, and governance indicators. Buildings, 15(19), 3601.

Mailagaha Kumbure, M., & Luukka, P. (2022). A generalized fuzzy k-nearest neighbor regression model based on Minkowski distance. Granular Computing, 7(3), 657-671.

Mandel, T., Kranzl, L., Popovski, E., Sensfuß, F., Müller, A., & Eichhammer, W. (2023). Investigating pathways to a net-zero emissions building sector in the European Union: what role for the energy efficiency first principle?. Energy Efficiency, 16(4), 22.

Martinho, M., Fernandes, J., Gomes, R., Lourador, P., & Ferrão, P. (2025). A comprehensive tool for embodied carbon quantification and supporting mitigation strategies in the Building sector. Discover Applied Sciences, 7(7), 781.

Meena, C. S., Kumar, A., Jain, S., Rehman, A. U., Mishra, S., Sharma, N. K., ... & Eldin, E. T. (2022). Innovation in green building sector for sustainable future. Energies, 15(18), 6631.

Mohammed, M. A., Abd Ghani, M. K., Hamed, R. I., Mostafa, S. A., Ibrahim, D. A., Jameel, H. K., & Alallah, A. H. (2017). Solving vehicle routing problem by using improved K-nearest neighbor algorithm for best solution. Journal of Computational Science, 21, 232-240.

Moran, D., Pichler, P. P., Zheng, H., Muri, H., Klenner, J., Kramel, D., ... & Gurney, K. R. (2022). Estimating CO2 emissions for 108 000 European cities, Earth Syst. Sci. Data, 14, 845–864.

Morelli, C., Maranzano, P., & Otto, P. (2025). Spatiotemporal clustering of GHGs emissions in Europe: exploring the role of spatial component. arXiv preprint arXiv:2503.11909.

Myint, N. N., Shafique, M., Zhou, X., & Zheng, Z. (2025). Net zero carbon buildings: A review on recent advances, knowledge gaps and research directions. Case Studies in Construction Materials, e04200.

Nader, Y., Sixt, L., & Landgraf, T. (2022, June). DNNR: Differential nearest neighbors regression. In International Conference on Machine Learning (pp. 16296-16317). PMLR.

Nagaj, R., Gajdzik, B., Wolniak, R., & Grebski, W. W. (2024). The impact of deep decarbonization policy on the level of greenhouse gas emissions in the European Union. Energies, 17(5), 1245.

Nichifor, B., Zait, L., & Turcu, O. (2025). Renewable Investments, Environmental Spending, and Emissions in Eastern Europe: A Spatial-Economic Analysis of Management and Policy Decisions Efficiency. Sustainability, 17(7), 3010.

Ozturk Kiyak, E., Ghasemkhani, B., & Birant, D. (2023). High-Level K-Nearest Neighbors (HLKNN): A supervised machine learning model for classification analysis. Electronics, 12(18), 3828.

Papachatzis, K. (2024). Machine learning-based price prediction for thermal insulation materials: a holistic approach integrating thermophysical, technical, and environmental attributes in the Greek construction market. Energy and Buildings, 324, 114899.

Papangelopoulou, M. D., Alexakis, K., & Askounis, D. (2025). Assessment Methods for Building Energy Retrofits with Emphasis on Financial Evaluation: A Systematic Literature Review. Buildings, 15(14), 2562.

Park, H. G., Shin, K. S., & Kim, J. C. (2025). Efficient Clustering Method for Graph Images Using Two-Stage Clustering Technique. Electronics, 14(6), 1232.

Petrescu, A. M. R., Peylin, P., Matthews, B., Dentener, F., Balkovic, J., Bastrikov, V., ... & Walther, S. (2023). The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990–2020. Earth System Science Data, 15(10), 4295-4370.

Petruška, I., Litavcová, E., & Chovancová, J. (2022). Impact of renewable energy sources and nuclear energy on CO2 emissions reductions—the case of the EU countries. Energies, 15(24), 9563.

Piccardo, C., Alam, A., & Hughes, M. (2021). The Potential Contribution of Wood in Green Building Certifications: prospects in sustainable residential buildings. Architectural Research in Finland, 5(1), 130-146.

Rakhshan, K., Daneshkhah, A., & Morel, J. C. (2023). Stakeholders’ impact on the reuse potential of structural elements at the end-of-life of a building: A machine learning approach. Journal of Building Engineering, 70, 106351.

Raycheva, I. Economic Growth, Energy Consumption and CO2 Emissions in the European Union. A Panel Data Analysis. In LIMEN 2023/9–Leadership, Innovation, Management and Economics: Integrated Politics of Research-CONFERENCE PROCEEDINGS (pp. 245-251). Udruženje ekonomista i menadžera Balkana.

Reyes, N., Rodríguez, B., Wiegand, E., Zilic, F., Ramage, M., Bukauskas, A., ... & Gin, Y. (2021). Achieving zero carbon emissions in the construction sector: The role of timber in decarbonising building structures.

Rheude, F., & Röder, H. (2022). Estimating the use of materials and their GHG emissions in the German building sector. Cleaner Environmental Systems, 7, 100095.

Roca Reina, J. C., Carlsson, J., Volt, J., & Toleikyte, A. (2025). Alternatives for Decarbonising High-Temperature Heating Facilities in Residential Buildings. Energies, 18(2), 235.

Rossbroich, J., Durieux, J., & Wilderjans, T. F. (2022). Model selection strategies for determining the optimal number of overlapping clusters in additive overlapping partitional clustering. Journal of Classification, 39(2), 264-301.

Sadhukhan, S., & Yadav, V. K. (2023). Forecasting, capturing and activation of carbon-dioxide (CO $ _2 $): Integration of Time Series Analysis, Machine Learning, and Material Design. arXiv preprint arXiv:2307.14374.

Sarıca, K., Harputlugil, G. U., İnaner, G., & Kollugil, E. T. (2023). Building sector emission reduction assessment from a developing European economy: A bottom-up modelling approach. Energy Policy, 174, 113429.

Sasmita, N. R., Khairul, M., Sofyan, H., Kruba, R., Mardalena, S., Dahlawy, A., ... & Maula, A. W. (2023). A Statistical Clustering Approach: Mapping Population Indicators Through Probabilistic Analysis in Aceh Province, Indonesia. Infolitika Journal of Data Science, 1(2), 63-72.

Sechi, S., Giarola, S., & Leone, P. (2022). Taxonomy for Industrial Cluster Decarbonization: An Analysis for the Italian Hard-to-Abate Industry. Energies, 15(22), 8586.

Seyedabadi, M. R., Karrabi, M., Shariati, M., Karimi, S., Maghrebi, M., & Eicker, U. (2024). Global building life cycle assessment: Comparative study of steel and concrete frames across European Union, USA, Canada, and Australia building codes. Energy and Buildings, 304, 113875.

Sharmina, M., Pappas, D., Scott, K., & Gallego-Schmid, A. (2023). Impact of circular economy measures in the European Union built environment on a net-zero target. Circular Economy and Sustainability, 3(4), 1989-2008.

Song, X., Zhai, S., & Zhou, N. (2024). The carbon emissions from public buildings in China: a systematic analysis of evolution and spillover effects. Sustainability, 16(15), 6622.

Tudor, C., Sova, R., Stamatiou, P., Vlachos, V., & Polychronidou, P. (2025). Future-Proofing EU-27 Energy Policies with AI: Analyzing and Forecasting Fossil Fuel Trends. Electronics, 14(3), 631.

Tzeiranaki, S. T., Bertoldi, P., Economidou, M., Clementi, E. L., & Gonzalez-Torres, M. (2023). Determinants of energy consumption in the tertiary sector: Evidence at European level. Energy Reports, 9, 5125-5143.

Uddin, S., Haque, I., Lu, H., Moni, M. A., & Gide, E. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Scientific Reports, 12(1), 6256.

Vagnini, C., Canal Vieira, L., Longo, M., & Mura, M. (2025). Regional drivers of industrial decarbonisation: a spatial econometric analysis of 238 EU regions between 2008 and 2020. Regional Studies, 59(1), 2380369.

Velemir Radović, M., Nikolic, D., & Jovanović, S. (2024). ENERGY EFFICIENCY IN THE BUILDING SECTOR IN SERBIA-AN OVERVIEW.

Velentzas, P., Moutafis, P., & Mavrommatis, G. (2020, November). An improved GPU-based algorithmfor processing the k nearest neighbor query. In Proceedings of the 24th Pan-Hellenic Conference on Informatics (pp. 372-375).

Vigna, I., Lollini, R., & Pernetti, R. (2021). Assessing the energy flexibility of building clusters under different forcing factors. Journal of Building Engineering, 44, 102888.

Wei, J., Shi, W., Ran, J., Pu, J., Li, J., & Wang, K. (2023). Exploring the driving factors and their spatial effects on carbon emissions in the building sector. Energies, 16(7), 3094.

Xia, Y., Yang, Z., Jiang, X., & Wang, H. (2024). The road to carbon neutrality in China’s building sector. Iscience, 27(9).

Xin, L., Li, S., Rene, E. R., Lun, X., Zhang, P., & Ma, W. (2023). Prediction of carbon emissions peak and carbon neutrality based on life cycle CO2 emissions in megacity building sector: Dynamic scenario simulations of Beijing. Environmental Research, 238, 117160.

Yakymchuk, A., & Rataj, M. A. (2025). Economic Analysis of Fossil CO2 Emissions: A European Perspective on Sustainable Development. Energies, 18(8), 2106.

Yang, S., Yang, D., Shi, W., Deng, C., Chen, C., & Feng, S. (2023). Global evaluation of carbon neutrality and peak carbon dioxide emissions: Current challenges and future outlook. Environmental Science and Pollution Research, 30(34), 81725-81744.

Yao, Z., Li, W., & Pang, Y. (2024). Environmental modeling of impacts of agricultural land changes using Markov chain and machine learning (case study: Shanghai metropolis, China). International Agrophysics, 38(4), 353-371.

You, K., Li, Y., Cai, W., Zhang, L., Liu, Z., Feng, W., & Wei, Y. M. (2025). Mitigating emissions and costs through demand-side solutions in Chinese residential buildings. Nature communications, 16(1), 7358.

Zangana, H. M., & Abdulazeez, A. M. (2023). Developed clustering algorithms for engineering applications: A review. International Journal of Informatics, Information System and Computer Engineering (INJIISCOM), 4(2), 160-182.

Żelazna, A., & Pawłowski, A. (2025). Review of the Role of Heat Pumps in Decarbonization of the Building Sector. Energies, 18(13), 3255.

Zhang, T., & Lin, G. (2021). Generalized k-means in GLMs with applications to the outbreak of COVID-19 in the United States. Computational Statistics & Data Analysis, 159, 107217.

Zhu, C., Chang, Y., Li, X., & Shan, M. (2022). Factors influencing embodied carbon emissions of China's building sector: An analysis based on extended STIRPAT modeling. Energy and Buildings, 255, 111607.